§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-1 

### C HAPTER 

### C ONTENTS B2E-1 Summary Insights from this Profile . . . . . . . . . . B2E-2 B2E-2 Domestic Production . . . . . . . . . . . . . . . . . . . . . B2E-3 B2E-2.1 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-4 B2E-2.2 Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-7 B2E-2.3 Number of Facilities and Firms . . . . . . . . . B2E-8 B2E-2.4 Employment and Productivity . . . . . . . . . B2E-12 B2E-2.5 Capital Expenditures . . . . . . . . . . . . . . . . B2E-14 B2E-2.6 Capacity Utilization . . . . . . . . . . . . . . . . B2E-16 B2E-3 Structure and Competitiveness . . . . . . . . . . . . . B2E-17 B2E-3.1 Geographic Distribution . . . . . . . . . . . . . B2E-17 B2E-3.2 Facility Size . . . . . . . . . . . . . . . . . . . . . . B2E-18 B2E-3.3 Firm Size . . . . . . . . . . . . . . . . . . . . . . . . B2E-20 B2E-3.4 Concentration Ratios . . . . . . . . . . . . . . . B2E-20 B2E-3.5 Foreign Trade . . . . . . . . . . . . . . . . . . . . . B2E-21 B2E-4 Financial Condition and Performance . . . . . . . B2E-24 B2E-5 Facilities Operating Cooling Water Intake Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-26 B2E-5.1 Waterbody and Cooling System Type . . . B2E-27 B2E-5.2 Facility Size . . . . . . . . . . . . . . . . . . . . . . B2E-28 B2E-5.3 Firm Size . . . . . . . . . . . . . . . . . . . . . . . . . B2E-29 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2E-30 

# Chapter B2E: Aluminum (SIC 333/5)

EPA’s Detailed Industry Questionnaire , hereafter referred to as the DQ, identified two 4- digit SIC codes in the nonferrous metals industries (SIC codes 333/335) with at least one existing facility that operates a CWIS, holds a NPDES permit, withdraws equal to or greater than two million gallons per day (MGD) from a water of the United States, and uses at least 25 percent of its intake flow for cooling purposes (facilities with these characteristics are hereafter referred to as facilities potentially subject to the Phase III regulation or “potential Phase III facilities”). For each of the two SIC codes, Table B2E-1 below provides a description of the industry segment, a list of products manufactured, the total number of detailed questionnaire respondents (weighted to represent national results), and the number and percent of potential Phase III facilities within the estimated national total of facilities in the respective industry SIC code groups.

Table B2E-1: Potential Phase III facilities in the Aluminum Industries (SIC 333/335)

SIC SIC Description Important Products Manufactured Number of Facilities a
Total Potential Phase III facilities b %

3334 Primary Production of Aluminum Producing aluminum from alumina and in refining aluminum by any process 31 11 35.5%

3353 Aluminum Sheet, Plate, and Foil Flat rolling aluminum and aluminum-base alloy basic shapes, such as rod and bar, pipe and tube, and tube blooms; producing tube by drawing 57 10 17.5%

Total 88 21 24.0%

aNumber of weighted detailed questionnaire survey respondents. b Individual numbers may not a dd up due to independent rounding. Source: U.S. EPA, 2000; Executive O ffice of the President, 1987.
As reported in the table, EPA estimates that 21 out of 88 facilities (or 24 percent) in the Aluminum Industries (SIC 333/335) are potentially subject to this proposed regul ation. EPA also estimated the percentage of total production that occurs at facilities potentially subject to th e proposed regulation. Total value of shipments for the Aluminum Industries (SIC 333/335) from the 1998 Annual Survey of Manufacturers is $19.3 billion. Value of shipments, a measure of the dollar value of production, w as selected for the basis of th is estimate. Because value of shipments data were not collected using the DQ, these data were not available for the sample of Phase III manufacturing facilities potentially subject to the proposed regulation. Total revenue, as reported on the DQ, was§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-2 used as a close approximation for value of shipments fo r these facilities. EPA estimated the total revenue of facilities in the aluminum industry subject to the proposedregulation is $12.1 billion. Therefore, EPA estimates that 63 percent of total domestic aluminum production occurs at facilities potentially subject to the proposed regulation. Table B2E-2 provides the cross-walk between SIC cod es and the new NAICS codes for the profiled aluminum SIC codes. The table shows that both of the profiled 4- digit SIC codes in the aluminum industry have a one-toone relationship to NAICS codes.

Table B2E-2: Relationships between SIC and NAICS Codes for the Aluminum Industries (1997)

SIC Code SIC Description NAICS Code NAICS Description Number of Establishments Value of Shipments

($1000) Employment

3334 Primary aluminum 331312 Primary aluminum production 235 7,565,377 15,763

3353 Aluminum sheet, plate, and foil 331315 Aluminum sheet, plate, and foil manufacturing 70 13,755,566 25,111
Source: U.S. DOC, 1997.



### B2E-1 S UMMARY 

### I NSIGHTS FROM THIS 

### P ROFILE A key purpose of this profile is to provide insight into the ability of aluminum industry firms subject to the 316(b) regulation to absorb compliance costs without material a dverse economic/financial effects. Two important factors in the industry’s ability to withstand compliance costs are: (1) the extent to which the industry can shift compliance costs to its customers through price increases, and (2) the financial health of the industry and its general business outlook. Likely Ability to Pass Complian ce Costs Through to Customers As reported in the following sections of this profile, th e aluminum industry is moderately concentrated. Thispotentially supports firms in this industry passing through to customers a significant portion of their compliancerelated costs. However, the domestic Primary Aluminum Production segment faces significant competition from imports into the U.S. market. Facilities in the Aluminum Sheet, Plate, and Foil segment have a notable reliance on foreign markets. The substantial competitive pressure from abroad weakens the potential of firms in this industry to pass through to customers a significant portion of their compliance-related costs. As discussed above, the proportion of total value of shipments in the indus try potentially subject to the proposed regulation is 63 percent. The actual proportion of total value of shipme nts subject to regulation-induced compliance costs would be smaller since not all of the facilities would be subject to the national categorical requirements of the proposed regulation: that is, facilities below the proposed design intake flow (DIF) would be subject to permitting based on best professional judgement (BPJ) rather than based on national standards, and several facilities currently employ baseline technologies that meet the requirements of th e proposed regulation. Given the likelihood that these percentages represent upper bound estimates, EPA believes that the theoretical threshold for justifying the use of industry-wide CPT rates in the impact analysis of exis ting Phase III aluminum facilities has not been met. For these reasons, in its analysis of regulatory impacts for the aluminum industry, EPA assumes that complying firms would be unable to pass compliance costs through to cu stomers: i.e., complying facilities must absorb all compliance costs within their financial condition at th e time of compliance (see following sections and Appendix 3 to Chapter B3: Economic Impact Analysis for Manufacturers for further information). Financial Health and General Business Outlook Over the past decade, the aluminum industry, like other U.S. manufacturing industries, has experienced a range of economic/financial conditions, including substantial ch allenges. In the early 1990s, the domestic aluminum§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-3 industry was adversely affected by reduced U.S. dema nd and the dissolution of the Soviet Union, resulting in large increases in Russian aluminum exports. Although domestic market conditions improved by mid-decade, weakness in Asian markets, along with growing Russian exports, dampened performance during the latter half of the 1990s. Demand for aluminum industry products dec lined again in 2000 through 2002, reflecting weakness in both the U.S. and world economies, and again resulted in oversupply and declining financial performance. More recently, as the U.S. economy began recovering from ec onomic weakness, the domestic aluminum industry is showing signs of recovery with higher demand levels and improving fi nancial performance over the course of 2003. Although the industry has weathered difficult periods over the past few years, the strengthening of the industry’s financial condition and ge neral business outlook suggest improve d ability to withstand additional regulatory compliance costs without imposing significant financial impacts.



### B2E-2 D OMESTIC 

### P RODUCTION Commercial production of aluminum using the electrolytic reduction process, known as the Hall-Heroult process, began in the late 1800s. The production of primary al uminum involves mining bauxite ore and refining it into alumina, one of the feedstocks for aluminum metal. Dir ect electric current is used to split the alumina into molten aluminum metal and carbon dioxide. The molten aluminum metal is then collected and cast into ingots. Technological improvements over the years have improved the efficiency of aluminum smelting, with a particular emphasis on reducing energy requirements. Currently, no commercially viable alternative exists to the electrometallurgical process (Aluminum Association, 2001). In 2003, aluminum recovered from purchased scrap was about 2.8 million tons, of which about 60% came from new (manufacturing) scrap and 40% from old scrap (di scarded aluminum products). Aluminum recovered from old scrap was equivalent to about 17% of apparent consumption (USGS, 2004a). Recycling consists of melting used beverage cans and scrap generated from operations. Recycling saves approximately 95 percent of the energy costs involved in primary smelting from bauxite (S&P, 2001). In contrast to the steel industry, aluminum minimills have had limited impact on the profitability of traditional integrated aluminum producers. Aluminum minimills are not able to produce can sheet of the same quality as that produced by integrated facilities. As a result, they are able to compete only in production of commodity sheet products for the building and distributor markets, which are considered mature markets. Acco rding to Standard & Poor’s (2001), construction of new minimill capacity is unlikely given the potential that a dded capacity would drive down prices in the face of slow growth in the markets for minimill products. No secondary smelters (included, along with secondary smelting of other metals, in SIC code 3341) were reported in EPA’s Detailed Industry Questionnaire . These facilities are therefore not addressed in this profile. Facilities in SIC code 3353 produce semifabricated products from primary or secondary aluminum. Examples of semifabricated aluminum products include (Aluminum Association, undated):

< sheet (cans, construction materials, and automotive parts);

< plate (aircraft and spacecraft fuel tanks);

< foil (household aluminum foil, building insulation, and automotive parts);

< rod, bar, and wire (electrical transmission lines); and

< extrusions (storm windows, bridge structures, and automotive parts). U.S. aluminum companies are generally vertically inte grated. The major aluminum companies own large bauxite reserves, mine bauxite ore and refine it into alumina, produce aluminum ingot, and operate the rolling mills and finishing plants used to produce semifabricated aluminum products (S&P, 2001). As noted, the production of primary aluminum is an electrometallurgical process, which is extremely energy intensive. Electricity accounts for approximately 30 percent of total production costs for primary aluminum smelting. The aluminum industry is therefore a major i ndustrial user of electricity, spending more than $2 billion annually. The industry has pursued opportunities to reduce its use of electricity as a means of lowering costs. In§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-4 the last 50 years, the average amount of electricity n eeded to make a pound of aluminum has declined from 12 kilowatt hours to approximately 7 kilowatt hours. (Aluminum Association, undated). B2E-2.1 Output The largest single source of demand for aluminum is the transportation segment, primarily the manufacture of motor vehicles. Demand for lighter, more fuel effici ent vehicles has increased demand for aluminum in auto manufacturing, at the expense of steel (S&P, 2001). Until 1996, containers were the largest U.S. market for aluminum. Production of beverage cans is a major use of aluminum sheet, and aluminum has entirely replaced steel in the beverage can market. Other major uses of aluminum include constructi on (including aluminum siding, windows, and gutters) and cons umer durables (USGS, 2001a). Demand for aluminum reflects the overall state of the domestic and world economies, as well as long-term trends in materials use in major end-use sectors. Because al uminum production involves large fixed investments and capacity adapts slowly to fluctuations in demand, th e industry has experienced alternating periods of excess capacity and tight supplies. The early 1980s was a pe riod of oversupply, high inventories, and excess capacity. By 1986, excess capacity was closed, inventories were lo w, and demand increased substantially. The early 1990s were affected by reduced U.S. demand and the dissolution of the Soviet Union, resulting in large increases in Russian exports of aluminum. By the mid-1990s, gl obal production declined, de mand rebounded, and aluminum prices rose. Subsequent increased production reflected an overall increase in the demand for aluminum with stronger domestic economic growth, driven by increased consumption by the transportation, container, and construction segments. The economic crises in Asian markets in the later 1990s, along with growing Russian exports, again resulted in a period of oversupply, although U.S. demand for aluminum remained strong. Demand declined again in 2000 through 2002 due to slower growthin both the U.S. and the world economy, resulting in oversupply. The surplus was mitigated somewhat as demand in the automotive and housing markets remained relatively high through mid-2003. In addition, the Calif ornia energy crisis in 2000 and 2001 reduced production from primary smelters located in the Pacific Northwest (Aluminum Association, 1999; USGS, 1999c; USGS, 1998e; USGS, 1994c; Value Line, 2001). Production in China increased during this period, and although increased Chinese consumption helped re duce the surplus slightly, the country switched from being a net importer to a net exporter. Additionally, interest rates ar e likely to increase which may decrease U.S. demand for aluminum from major industrial end markets (aerosp ace, automotive, home-construction, and commercialconstruction). However, with the economy showing signs of recovery th e aluminum industry saw higher demand levels in 2003. If the economy remains strong, demand is expected to continue at 2003 levels (Value Line, 2003; S&P 2004). Table B2E-3 shows trends in output of aluminum by Primary Aluminum producers and recovery of aluminum from old and new scrap. Secondary production grew from 37 percent to over half of total domestic production over the period from 1990 to 2003. Of total secondary pr oduction in 2003, 1,170 thousand metric tons (MT) or 40 percent, is from old scrap (discarded aluminum products), as opposed to new scrap (from manufacturing). Primary production of aluminum recorded a net decrease over the 13-year period, but declined sharply in 2001 compared to 2000. As noted above, this decrease reflect s reduced domestic and world demand for aluminum, and curtailed production at a number of Pacific Northwest m ills caused by the California energy crisis (S&P 2001a; USGS, 2001a). Production remained fairly constant for the final three years of the period.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

1 Terms highlighted in bold and italic font are further explained in the glossary.

B2E-5 Table B2E-3: U.S. Quantities of Aluminum Produced

Year Aluminum Ingot Primary Production Secondary Production (from old & new scrap) Total Production
Thousand MT % Change Thousand MT % Change Thousand MT % Change 1990 4,048 n/a 2,390 n/a 6,438 n/a 1991 4,121 1.8% 2,290 -4.2% 6,411 -0.4% 1992 4,042 -1.9% 2,760 20.5% 6,802 6.1% 1993 3,695 -8.6% 2,940 6.5% 6,635 -2.5% 1994 3,299 -10.7% 3,090 5.1% 6,389 -3.7% 1995 3,375 2.3% 3,190 3.2% 6,565 2.8% 1996 3,577 6.0% 3,310 3.8% 6,887 4.9% 1997 3,603 0.7% 3,550 7.3% 7,153 3.9% 1998 3,713 3.1% 3,440 -3.1% 7,153 0.0% 1999 3,779 1.8% 3,700 7.6% 7,479 4.6% 2000 3,688 -2.4% 3,450 -6.8% 7,138 -4.6% 2001 2,637 -28.5% 2,970 -13.9% 5,607 -21.4% 2002 2,707 2.7% 2,930 -1.3% 5,637 0.5% 2003 2,703 -0.1% 2,930 0.0% 5,633 -0.1% Total percent change 1990-2003 -33.2% 22.6% -12.5% Average annual

growth rate -3.1% 1.6% -1.0% Source: USGS, 2001b-2003b; USGS 1996a-2004a; USGS 2002d
Value of shipments and value added are two common measures of manufacturing output 1 . They provide insight into the overall economic health and outlook for an industr y. Value of shipments is the sum of the receipts a manufacturer earns from the sale of its outputs; it indicates the overall size of a market or the size of a firm in relation to its market or competitors. Value added measures the value of production activity in a particular industry. It is the difference between the value of sh ipments and the value of inputs used to make the products sold. Figure B2E-1 reports constant dollar value of shipme nts and value added for the Primary Aluminum, and Aluminum Sheet, Plate, and Foil segments between 1987 and 2001.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-6 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 VOS-Primary Aluminum Production (SIC 3334) VOS-Primary Aluminum Production (Estimated from NAICS) VOS-Aluminum Sheet, Plate, and Foil (SIC 3353) VOS-Aluminum Sheet, Plate, and Foil (Estimated from NAICS)

0 200 400 600 800 1,000 1,200 1,400 1,600

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 VA-Primary Aluminum Production (SIC 3334) VA-Primary Aluminum Production (NAICS to SIC 3334) VA-Aluminum Sheet, Plate, and Foil (SIC 3353) VA-Aluminum Sheet, Plate, and Foil (NAICS to SIC 3353) Figure B2E-1: Value of Shipments and Valu e Added for Profiled Aluminum Segments (millions,$2003) Value of Shipments

Value Added

a Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the North American Industry Classification System (NAICS). For this analysis, EPA convert ed the NAICS classification data to the SIC code classifications us `ing the 1997 Economic Census Bridge Between NAICS and SIC

. Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992 and 1997.
The value of Primary Aluminum shipments shows generallythe same pattern as the quantity data shown in Table B2E-3. Trends in production reflect trends in demand for aluminum, growth since 1990 in the percentage of domestic demand provided by imports, and increasing sec ondary production of aluminum, which substitutes in some but not all markets for primary production. Va lue added by aluminum production excludes the value of purchased materials and services (including electricity), and shows less fluctuation since 1990 than value of shipments. Demand for semifinished aluminum products reflects demand from the transportation, container, and building industries. Real value of shipments of Aluminum Sh eet, Plate, and Foil declined from the late 1980s through 1993, and then recovered by mid-decade, before turni ng down again in the late 1990s. Demand for semifinished§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-7 products has been affected by strong growth in both th e container and packaging segment and the auto segment (S&P, 2001). Both industry segments show lower values for the constantdollar value of shipments and value added at the end of the 15-year analysis period than at the beginning of the period. These declining values reflect the overall maturity of the aluminum production industry and the in creasing role of foreign production in meeting total U.S. demand.
B2E-2.2 Prices The producer price index (PPI) measures price changes, by segment, from the perspective of the seller, and indicates the overall trend of product pricing, and thus supply-demand conditions, within a segment. The price trends shown for Primary Aluminum in Figur e B2E-2 reflect the fluctuations in world supply and demand discussed in the previous section. During the early 1980s, the aluminum industry experienced oversupply, high inventories, excess capacity, and weak demand, resulting in falling prices for aluminum. By 1986, much of the excess capacity had been permanentlyclosed, inventories had been worked down, and worldwide demand for aluminum increased strongly. This resulted in price increases through 1988, as shown in Figure B2E.2. In the early 1990s, the dissolution of the Soviet Union had a major impact on aluminum markets. Large quantities of Russian aluminum that formerly had been consumed internally, primarily in military applications, were sold in world markets to generate hard currency. At the sam e time, world demand for aluminum was decreasing. The result was increasing inventories and depressed aluminum prices. The United States and five other primary aluminum pr oducing nations signed an agreement in January 1994 to curtail global output, in response to the sharp decline in al uminum prices. At the time of the agreement, there was an estimated global overcapacity of 1.5 to 2.0 million metric tons per year (S&P, 2000). By the mid-1990s, production cutbacks, increased demand, and declining inventories led to a sharp rebound of prices. Prices declined again though during the late 1990s, when the economic crises in Asian markets reduced the demand for aluminum (USGS, 2001b). During 2000, pr ices rebounded sharply despite the continuing trend of high Russian production and exports. However, econom ic recession caused prices to fall again through 2002. Prices began to recover during 2003 and are expected to continue on an upward trend as the economy recovers. (S&P, 2001-2004).§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-8 80 90 100 110 120 130 140 150 160 170

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Primary Aluminum Production (SIC 3334) Aluminum Sheet, Plate, and Foil (SIC 3353) Figure B2E-2: Producer Price Indexes for Profiled Aluminum Segments

Source: BLS, 2002.

B2E-2.3 Number of Facilities and Firms U.S. Geological Survey data indicate that the number of Primary Aluminum facilities and the number of firms that own them remained fairly constant over the period 1995 through 2001, as shown in Table B2E-4. The number of domestic companies and plants sharply dec lined in 2002 compared to 2001. In 2002, the 10 domestic producers had a total of 7 smelters that were either te mporarily or permanently idled. The bulk of the idled capacity resulted from curtailed production at a number of Pacific Northwest mills caused by the California energy crisis. Most of the smelters outside of this region continued to operate at or near their engineered capacities (S&P 2001; USGS, 2001a; USGS, 2002c).§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-9 Table B2E-4: Primary Aluminum Production - Number of Companies and Plants Year Number of Companies Number of Plants 1995 13 22 1996 13 22 1997 13 22 1998 13 23 1999 12 23 2000 12 23 2001 12 23 2002 7 16 2003 7 15 Source: USGS, 1996a-2004a.
Statistics of U.S. Businesses covers a larger number of facilities classified under SIC 3334 than do the USGS data, and also provide data on SIC 3353 (Aluminum Sheet, Plate, and Foil). These data, shown in Table B2E-5 and B2E-6, show more fluctuation in the num ber of establishments and the number of firms. Table B2E-5 shows that the number of Primary Aluminum facilities decreased by 30 percent between 1991 and 1995, with the majority of this decrease, 27 percent, occurring between 1991 and 1993. The number of facilities in the Aluminum Sheet, Plate, and Foil segment showed a more consistent trend, increasing each year except in 1993. In 1998, the number of facilities decreased in both segments, but have continued to grow since then.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-10 Table B2E-5: Number of Facilities for Profiled Aluminum Segments

Year Primary Aluminum Production Al uminum Sheet, Plate, and Foil Number of Establishments Percent Change Number of Establishments Percent Change
1989 56 n/a 61 n/a 1990 54 -3.6% 64 4.9% 1991 57 5.6% 73 14.1% 1992 52 -8.8% 73 0.0% 1993 44 -15.4% 63 -13.7% 1994 41 -6.8% 69 9.5% 1995 40 -2.4% 76 10.1% 1996 51 27.5% 81 6.6% 1997 34 -33.3% 91 12.3% 1998 28 -17.6% 83 -8.8% 1998 a 28 -17.6% 79 -13.2% 1999 a 29 3.6% 93 17.7% 2000 a 32 10.3% 103 10.8% 2001 a 38 18.8% 111 7.8% Total Percent Change 1989-2001 -32.1% 82.0% Average Annual

Growth Rate -3.2% 5.1%

aBefore 1998, these data were compiled in the SIC system ; since 1998, these data have been compiled in the North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic Census Bridge Between NAICS and SIC

. Source: U.S. SBA, 1989-2001.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-11 The trend in the number of firms over the period between1989 and 2001 is similar to the trend in the number of facilities in both industry segments. Table B2E-6 on the following page presents SUSB information on the number of firms in each segment between 1989 and 2001.

Table B2E-6: Number of Firms for Profiled Aluminum Segments

Year Primary Aluminum Production Al uminum Sheet, Plate, and Foil Number of Firms Percent Change Number of Firms Percent Change 1990 38 n/a 43 n/a 1991 41 7.9% 53 23.3% 1992 36 -12.2% 53 0.0% 1993 33 -8.3% 45 -15.1% 1994 30 -9.1% 47 4.4% 1995 30 0.0% 51 8.5% 1996 40 33.3% 56 9.8% 1997 23 -42.5% 66 17.9% 1998 19 -17.4% 60 -9.1% 1998 a 19 -17.4% 56 -15.2% 1999 a 20 5.3% 66 17.9% 2000 a 22 10.0% 73 10.6% 2001 a 28 27.3% 82 12.3% Total Percent Change 1990-2001 -26.3% 90.7% Average Annual

Growth Rate -2.7% 6.0%

aBefore 1998, these data were compiled in the SIC system ; since 1998, these data have been compiled in the North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications us ing the 1997 Economic Census Bridge Between NAICS and SIC

. Source: U.S. SBA, 1989-2001.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-12 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Primary Production of Aluminum (SIC 3334) Primary Aluminum Production (NAICS to SIC 3334) Aluminum Sheet, Plate, and Foil (SIC 3353) Aluminum Sheet, Plate, and Foil (NAICS to SIC 3353) B2E-2.4 Employment and Productivity Figure B2E-3, below, provides information on employment from the Annual Survey of Manufactures for the Primary Aluminum and Aluminum Sheet, Plate, and Foil segments. Trends in Primary Aluminum facility employment reflect trends in both production and producer s’ efforts to improve labor productivity to compete with less labor-intensive minimills (McGraw-Hill, 2000). The figure shows that employment in the Primary Aluminum segment has declined steadily sin ce 1992, even in years of increased production. Employment in the Aluminum Sheet, Plate, and Foil segment declined from 1987 through 1994, but rose between 1995 and 1997, before declining again during 1997 to 2001. Employment in the Primary Aluminum Production segment increased during the 1987 to 1992 period, but fell persistently over the remainder of the 1990s decade and through 2001. For both industry segments, the low employment level in 2001 resulted from the idled capacity from curtailed production at a number of Pacific No rthwest mills caused by the California energy crisis.

Figure B2E-3: Employment for Profiled Aluminum Segments

a Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the North American Industry Classification System (NAICS). For this analysis, EPA convert ed the NAICS classification data to the SIC code classifications us ing the 1997 Economic Census Bridge Between NAICS and SIC

. Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992, and 1997.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-13 Table B2E-7 presents the change in value added per la bor hour, a measure of labor productivity, for the Primary Aluminum Production and Aluminum Sheet, Plate, and Foil segments between 1987 and 2001. The trend in labor productivity in both segments showed volatility over this pe riod, reflecting variations in capacity utilization. Value added per hour in the Primary Aluminum segment showed a 3.3 percent net increase over the entire period 1987 and 2001. Value added per hour in the Aluminum Sheet, Plate, and Foil segment saw a three percent decrease over the whole period between 1987 and 2001.

Table B2E-7: Productivity Trends for Profiled Aluminum Segments ($2003)

Year Primary Production of Aluminum Aluminum Sheet, Plate, and Foil
Value Added (millions) Production Hours (millions) Value Added/Hour Value Added (millions) Production Hours (millions) Value Added/Hour ($/hour) Percent Change ($/hour) Percent Change 1987 821 28 30 n/a 1,317 40 33 n/a 1988 933 32 29 -2% 1,345 41 33 0% 1989 964 30 32 9% 1,336 41 33 0% 1990 971 32 30 -5% 1,322 40 33 2% 1991 971 32 30 0% 1,251 39 32 -4% 1992 986 32 31 1% 1,241 40 31 -2% 1993 870 29 30 -2% 1,224 39 32 2% 1994 827 27 31 2% 1,148 37 31 -1% 1995 855 28 30 -2% 1,178 38 31 -2% 1996 844 29 30 -2% 1,215 39 31 1% 1997 783 26 30 1% 1,302 41 32 1% 1998 a 800 27 30 0% 1,187 39 31 -3% 1999 a 742 26 29 -5% 1,158 37 32 3% 2000 a 700 24 29 0% 1,110 35 32 1% 2001 a 607 19 31 9% 1,011 32 32 -2% Total Percent Change 1987-2001 -26.1% -32.1% 3.3% -23.2% -20.0% -3.0%
Average Annual

Growth Rate -2.1% -2.7% 0.2% -1.9% -1.6% -0.2%

aBefore 1998, the Department of Commerce co mpiled data in the SIC system; since 1998, th ese data have been compiled in the Nort h American Industry Classification System (NAI CS). For this analysis, EPA converted th e NAICS classification data to the SIC cod e classifications using the 1997 Economic Census Bridge Between NAICS and SIC

. Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992, and 1997.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-14 B2E-2.5 Capital Expenditures Aluminum production is a highly capital-intensive process. Capital expenditures are needed to modernize, replace, and when market conditions warrant, expand capac ity. Environmental requirements also require major capital expenditures. Capital expenditures in the Primary Aluminum Produc tion and Aluminum Sheet, Plate, and Foil segments between 1987 and 2001 are presented in Table B2E-8 below. The table shows that capital expenditures in the Primary Aluminum segment increased throughout the early 1990s, reaching a high in 1992 and again in 1998. In between these two periods of increased capital investment there was a significant decrease of 46 percent between 1992 and 1994. These decreases resulted from the production cutbacks and capacity reductions implemented in response to oversupply conditions prevalent in the market for aluminum. Capital expenditures in the Aluminum Sheet, Plate, and Foil segment also fluctuated considerably between 1987 and 2001, with the highest values occurring in 1990, two years earlier than in the Primary Aluminum segment. Although producers of Aluminum Sheet, Plate, and Fo il reduced capital expenditures by approximately 50 percent between 1988 and 1997, outlays began to increase in 2001.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-15 Table B2E-8: Capital Expenditures for Pr ofiled Aluminum Se gments (millions, $2003)

Year Primary Aluminum Production Al uminum Sheet, Plate, and Foil Capital Expenditures Percent Change Capital Expenditures Percent Change 1987 250 n/a 634 n/a 1988 205 -18.2% 731 15.4% 1989 245 19.4% 742 1.5% 1990 243 -0.6% 882 18.9% 1991 260 6.8% 710 -19.6% 1992 263 1.2% 512 -27.9% 1993 200 -24.0% 289 -43.6% 1994 141 -29.2% 321 11.1% 1995 174 23.0% 435 35.5% 1996 233 34.0% 451 3.7% 1997 355 52.2% 367 -18.5% 1998 a 432 21.8% 348 -5.3% 1999 a 380 -12.2% 352 1.2% 2000 a 371 -2.3% 367 4.1% 2001 a 266 -28.4% 594 62.1% Total Percent Change 1987-2001 6.4% -6.3% Average Annual

Growth Rate 0.4% -0.5%

aBefore 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the North American Industry Classification System (NAICS). For this analysis, EPA convert ed the NAICS classification data to the SIC code classifications us ing the 1997 Economic Census Bridge Between NAICS and SIC

. Source: U.S. DOC, 1988-1991, 1993-1996, and 1998-2001; U.S. DOC, 1987, 1992, and 1997. B2E-2.6 Capacity Utilization Capacity utilization measures actual output as a percentage of total potential output given the available capacity. Capacity utilization reflects excess or insufficient capacity in an industry and is an indication of whether new investment is likely. Capacity utilization is also closely linked to financial performance for industries with substantial fixed costs, such as the aluminum industry. Like integrated steel mills, the aluminum manufacturing process requires a large capital base to transform raw materi al into finished product. Because of the resulting high fixed costs of production, earnings can be very sensitive to production levels, with hi gh output levels relative to capacity needed for plants to remain profitable. Figure B2E-4 shows capacity utilization from 1989 to 2002 for the Primary Aluminum Production and Aluminum Sheet, Plate, and Foil segments. The figure shows that for most of the 1990s, the Primary Aluminum segment was characterized by excess capacity. Although capacity utilization for this segment was in the high 90 percent range between 1990 and 1992, domestic utilization fell sharply in 1993 as large amounts of Russian aluminum entered the global market for the first time (McGraw-Hill, 1999). Capacity utilization remained at this lower level through 1999. In 2000 and 2001, capacity utilization fell again reflecting the general weakening of product demand during the Asian economic crisis and later, general economic weakn ess in the U.S. and world economies. Reflecting the economic recovery, product demand increased and capacity utilization rose during 2002. A substantial amount of U.S. capacity remains idled, which could be brought on-line as demand improves. This “overhang” idle capacity is likely to limit construction of new capacity and to limit price increases for aluminum§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-16 55 60 65 70 75 80 85 90 95 100 105

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Primary Production of Aluminum (SIC 3334) Primary Production of Aluminum (NAICS to SIC 3334) Aluminum Sheet, Plate, and Foil (SIC 3353) Aluminum Sheet, Plate, and Foil (NAICS to SIC 3353) (S&P, 2001). No new smelter capacity has been cons tructed in the United States since 1980 (McGraw-Hill, 1999). The ten domestic producers of primary aluminum had a total of 7 smelters that were either temporarily or permanently idled in 2002. By the end of 2002, about 1.5 million metric tons per year of domestic primary aluminum smelting capacity, equivalent to 35 percent of total capacity, was closed. Of this total, 270,000 metric tons per year of capacity was permanently closed and th e remainder was classified as temporarily idled. The bulk of the idled capacity was due to curtailed production at a number of Pacific Northwest mills caused by the California energy crisis (USGS, 2002c). Although also experiencing year-to-year fluctuation, capac ity utilization in the Aluminum Sheet, Plate, and Foil segment grew overall between 1989 and 1998. This growth resulted largely from the continued strength of rolled aluminum products, which account for more than 50 per cent of all shipments from the aluminum industry. Increased consumption by the transportation segment, the la rgest end-use segment for aluminum sheet, plate, and foil, is responsible for bringing idle capacity into production (McGraw-Hill 1999) However, falling demand in these segments after 1998 and through 2001, led to a marked fa ll-off in capacity utilization. Again, reflecting the economic recovery during 2002, capacity utilization rose substantially in this segment.

Figure B2E-4: Capacity Utilization Rates (Fou rth Quarter) for Profiled Aluminum Segments

aBefore 1998, the Department of Commerce co mpiled data in the SIC system; since 1998, th ese data have been compiled in the Nort h American Industry Classification System (NAI CS). For this analysis, EPA converted th e NAICS classification data to the SIC cod e classifications using the 1997 Economic Census Bridge Between NAICS and SIC

. Source: U.S. DOC, 1989-2002.



### B2E-3 S TRUCTURE AND 

### C OMPETITIVENESS Aluminum production is a highly-concentrated industry. A number of large mergers among aluminum producers have increased the degree of concentration in the industry. For example, Alcoa (the largest aluminum producer) acquired Alumax (the third largest producer) in 1998 and Reynolds (the second largest producer) in May 2000. Alcan acquired Algroup in 2000 and Pechiney in 2004. Three companies now account for just over 50 percent of global aluminum output (S&P, 2004). Some sources speculate that, with increased consolidation resulting from mergers, aluminum producers might refrain from return ing idle capacity to production as demand for aluminum grows, which could reduce the cyclical volatility in production and aluminum prices that has characterized the industry in the past (S&P, 2000). B2E-3.1 Geographic Distribution§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

2 The 1992 Census of Manufactures is the most recent data available by SIC code and State.

B2E-17 

### 4 1

1 2

1 3 2

2 3 4 6
3

6 7

6 2 4

2 3 2 1 3

0 10

12

Number of Facilities 0 1 - 2 3 - 4 5 - 7 8 - 12 The cost and availability of electricity is a driving force behind decisions on the location of new or expanded smelter capacity. The Primary Aluminum producers are ge nerally located in the Pacific Northwest (OR, MT, WA) and the Ohio River Valley (IL, IN, KY, MI, MO, OH, PA), where supplies of lower-priced hydroelectric and coal-based electricity are abundant. In 2002, the 11 smelters east of the Mississippi River accounted for 75 percent of production; whereas the remaining 11 smelters , which included the 9 Pacific Northwest smelters, accounted for only 25 percent (USGS, 2004a). Aluminum consumption was centered in the East Central United States (USGS, 2004a). The Aluminum Sheet, Plate, and Foil segment is located principally in California and the Appalachian Region (Alabama, Kent ucky, Maryland, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia). Figure B2E-5 shows the distribution of all facilities by State in both profiled aluminum segments (primary smelters and Aluminum Sheet, Plate, and Foil producers), based on the 1992 Census of Manufactures. 2

Figure B2E-5: Number of Facilities by State for Aluminum Segments (SIC 3334 and 3353)

Source: U.S. DOC, 1987, 1992, and 1997.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-18 B2E-3.2 Facility Size Facility size can be expressed by the number of employees and/or by the total value of shipments, with the most accurate depiction of size being a combination of both. Economic Census data include numer ous small facilities (less than 10 employees) for the profiled aluminum segments, as shown in Figure B2E-6. These facilities may not be production facilities. Value of shipments, however, are dominated by large establishments (greater than 500 employees) for both the Primary Aluminum and Aluminum Sheet, Plate, and Foil industry segments. Figure B2E-6 shows that 93 percent of the value of shipments for the Primary Aluminum segment is produ ced by establishments with more than 250 employees. Approximately 88 percent of the value of shipments for the Aluminum Sheet, Plate, and Foil industry is produced by establishments with more than 250 employees. Esta blishments in the Primary Aluminum Production and the Aluminum Sheet, Plate, and Foil segments with more th an 1,000 employees are responsible for approximately 37 and 53 percent of all industry shipments, respectively.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-19 0 2 4 6 8 10 12 14 16

1-9 10-19 20-49 50-99 100-249 250-499 500-999 1000- 2499 2500+ Primary Aluminum Production (SIC 3334)
Aluminum Sheet, Plate, and Foil (SIC 3353)

$0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000

1-9 10-19 20-49 50-99 100-249 250-499 500-999 1,000- 2,499 2500+ Primary Aluminum Production (SIC 3334) Aluminum Sheet, Plate, and Foil (SIC 3353) Figure B2E-6: Number of Facilities and Value of Shipments in 1992 aby Facility Employment Size Category for Profiled Aluminum Segments
Number of Facilities

Value of Shipments (in millions)

aThe 1992 Census of Manufactures is the most recent data available by SIC code and facility employment size. Source: U.S. DOC, 1987, 1992, and 1997.

B2E-3.3 Firm Size The Small Business Administration (SBA) defines a small firm for SIC codes 3334 and 3353 as a firm with 1,000 or fewer and 750 or fewer employees, respectively. The Statistics of U.S. Businesses (SUSB) provide employment data for firms with 500 or fewer employees and do not specify data for companies with 500-750 employees for SIC 3353 and 500-1000 for SIC 3334. Therefore, based on 2001 data for firms with up to 500 employees,§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

3 Note that the measured concentration ratio and the HHF are very sensitive to how th e industry is defined. An industry with a high concentration in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign producers or if it competes with products produced byother industries (e.g., plastics vs. aluminumin beverage containers). Concentratio n ratios based on share of domestic production are therefore only one indicator of the extent of competition in an industry.

B2E-20 < 18 of the 28 firms in the Primary Aluminum Production segment had less than 500 employees. Therefore, at least 64 percent of this segment’s fi rms are classified as small. These small firms owned 18 facilities, or 47 percent of all facilities in the segment.

< 67 of the 82 firms in the Aluminum Sheet, Plate and Foil segment had less than 500 employees. Therefore, at least 82 percent of this segment’s fi rms are classified as small. These small firms owned 69 facilities, or 62 percent of all facilities in the segment. Table B2E-9 below shows the distribution of firms and facilities in SIC 3334 and 3353 by the employment size of the parent firm.

Table B2E-9: Number of Firms and Facilities by Employment Size Category for the Profiled Aluminum Segments, 2001 Employment Size Category Primary Aluminum Production Al uminum Sheet, Plate, and Foil Number of Firms Number of Facilities N umber of Firms Number of Facilities 0-19 13 13 42 43 20-99 4 4 16 16 100-499 1 1 9 10

500+ 10 20 15 42 Total 28 38 82 111 Source: U.S. SBA, 2001.

B2E-3.4 Concentration Ratios Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is closely related to entry barriers with more concentr ated industries generally having higher barriers. The four-firm concentration ratio (CR4) and the He rfindahl-Hirschman Index (HHI) are common measures of industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72 percent means that the four largest firms in the indus try account for 72 percent of the industry’s total value of shipments. The higher the concentration ratio, the less co mpetition there is in the industry, other things being equal 3 . An industry with a CR4 of more than 50 percent is generally considered concentrated. The HHI indicates concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market shares of 60, 30, and 10 percent, respectively, the HHI of this industry would be equal to 4,600 (60 2 + 30 2 + 10 2 ). The higher the index, the fewer the number of firms supplying the industry and the more concentrated the industry. Based on the U.S. Department of Justice’s guidelines for evaluating mergers, markets in which the HHI is under 1000 are considered unconcentrated, markets inwhich the HHI is between 1000 and 1800 are considered to be moderately concentrated, and those in which the HHIis in excess of 1800 are considered to be concentrated.
Table B2E-10 shows that Primary Aluminum has an HHI of 1231 and Aluminum Sheet, Plate and Foil has an HHI of 1447. The Primary Aluminum and Aluminum Sheet, Plate, and Foil segments, with HHI values of 1231§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-21 and 1447, respectively, appear to be moderately concentr ated. Thus, based on this factor, firms in the aluminum industry may enjoy moderate amounts of market power, wh ich may enable them to pass-through costs at a more than negligible rate. However, an accurate assessment of the cost pass-through potential of firms in the Aluminum industry must be considered in c onjunction with other measures of market power. The four largest firms in Primary Aluminum Production accounted for 59 percent of total U.S. primary capacity in 1992. Consolidation in the industry since the early 1990s h as increased concentration. With the merger of Alcoa, Inc. and Reynolds in May 2000, the single merged company accounted for 50 percent of domestic primary aluminum capacity, and the four largest U.S. producers c ontrol 72 percent of the domestic capacity (Alcoa Inc. for 50 percent, Century Aluminum Co. for almost 10 pe rcent, and Noranda Aluminum Inc. and Ormet Primary Aluminum Corp. for 6 percent each) reported at the end of 2002 (USGS, 2002c).

Table B2E-10: Selected Ratios for the Profiled Aluminum Segments

SIC Code Year Total Number of Firms Concentration Ratios 4 Firm (CR4) 8 Firm (CR8) 20 Firm (CR20) 50 Firm (CR50) HerfindahlHirschman Index

3334 1987 34 74% 95% 99% 100% 1934 1992 30 59% 82% 99% 100% 1456 1997 13 59% 82% 100% 100% 1231

3353 1987 39 74% 91% 99% 100% 1719 1992 45 68% 86% 99% 100% 1633 1997 41 65% 85% 98% 100% 1447 Source: U.S. DOC, 1987, 1992, and 1997.
B2E-3.5 Foreign Trade This profile uses two measures of foreign compe tition: export dependence and import penetration. Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests that higher import penetration levels will reduce market power and pricing discretion because foreign competition limits domestic firms’ ability to exercise such power. Firms belonging to segments in which imports account for a relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to passthrough costs because foreign producers would not incur costs as a result of the Phase III regulation. The estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for 2001 is 22 percent. For characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with import ratios close to or above 22 percent would more lik ely face stiff competition from foreign firms and thus be less likely to succeed in passing compliance costs through to customers. Export dependence, calculated as exports divided by value of shipments, measures the share of a segment’s sales that is presumed subject to strong foreign competiti on in export markets. The Phase III regulation would not increase the production costs of foreign producers with whom domestic firms must compete in export markets. As a result, firms in industries that rely to a greater extent on export sales would have less latitude in increasing prices to recover cost increases resulting from regulati on-induced increases in production costs. The estimated export dependence ratio for the entire U.S. manufacturing sector for 2001 is 15 percent. For characterizing the ability of industries to withstand compliance cost burdens , EPA judges that industries with export ratios close to§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-22 or above 15 percent are at a relatively greater disadva ntage in potentially recovering compliance costs through price increases since export sales are presumed subject to substantial competition from foreign producers. Table B2E-11 reports export dependen ce and import penetration for both the Primary Aluminum Production and the Aluminum Sheet, Plate, and Foil segments, since 1993. Imports of Primary Aluminum rose dramatically in 1994, primarily due to the large exports from Russian producers. Representatives of major aluminum producing countries met in late 1993 and 1994 to address the excess global supply of primary aluminum. Those discussions resulted in the Russian Federation’s agreement to reduce production by 500,000 MTs per year, and plans for other producers to cut their production and to assist Russian pr oducers to improve their environmental performance and stimulate the development of internal demand for th e Russian production (USGS, 1994c). Nonetheless, imports have continued to represent a substantial and growing proportion of U.S. demand, reaching an estimated 44 percent in 2001 for Primary Aluminum Production. By2002, Canada was the largest supplier of imports, supplying more than one-half of total imports. Russia continued to be the second largest supplier of aluminum materials to the U.S. (USGS, 2002c). The majority of U.S. exports (two-thirds) are shipped to Canada and Mexico. As discussed previously, the import penetration ratio for the Primary Aluminum Production segment in 2001 was 44 percent, which is twice the U.S. manufacturing segment average of 22 percent. The export ratio for Primary Aluminum Production in 2001 was eight percent; therefore the segment will not likely be affected by competitive pressures from abroad in export sales. On balance, the U.S. Primary Aluminum Production segment is subject to significant international competitive pressure, largely manifesting though the penetration of foreign product into domestic markets. This finding indicates a low likelihood that Primary Aluminum producers subject to the 316(b) regulation would be able to pass a material share of compliance costs through to customers. In 2001, the import penetration ratio for facilities in the Aluminum Sheet, Plate, and Foil segment was 11 percent, which is one-half of the U.S. manufacturing segment aver age of 22 percent. In 2001, the export dependence ratio for this segment was 15 percent, or approximately the average for U.S. manufacturers. This industry segment appears to face lower competition from foreign producer s in domestic markets than the Primary Aluminum Production segment, but this segment competes more vigorously in foreign markets, where it is more exposed to foreign competition than the Primary Aluminum Production seg ment. On balance, this industry segment is likely to face moderate competitive pressure from foreign pr oducers, whether in domestic or export markets, in attempting to recover regulation-induced increases in production costs through price increase. Overall, the competitive pressure from foreign firms/mark ets may offset the finding, stated above, that the aluminum industry would appear to possess market power fro m being a moderately concentrated industry. As a result, from a total market perspective, the industr y is not likely to possess any substantial market power advantage in being able to pass complian ce costs through to customers as price increases.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-23 Table B2E-11: Import Share and Export Depe ndence for the Profiled Aluminum Segments

Year Value of Imports (millions, $2003) Value of Exports (millions, $2003) Value of Shipments (millions, $2003) Implied Domestic Consumption a Import Penetration b Export Dependence c Primary Aluminum Production 1993 d 2,570 647 6,181 8,104 31.7% 10.5% 1994 d 4,073 627 6,533 9,979 40.8% 9.6% 1995 d 4,233 791 7,660 11,102 38.1% 10.3% 1996 d 3,422 768 6,679 9,333 36.7% 11.5% 1997 d 3,876 671 6,893 10,098 38.4% 9.7% 1997 e 3,892 688 6,893 10,097 38.5% 10.0% 1998 e 4,075 597 6,803 10,281 39.6% 8.8% 1999 e 4,193 648 6,272 9,817 42.7% 10.3% 2000 e 4,435 656 6,369 10,148 43.7% 10.3% 2001 e 4,152 474 5,707 9,385 44.2% 8.3% Total Percent Change
1993-2001 61.6% -26.7% 15.8% -20.7%
Average Annual Percent Change 5.5% -3.4% 1.6% -2.5% Aluminum Sheet, Plate, and Foil 1993 d 1,001 1,770 14,401 13,632 7.3% 12.3% 1994 d 1,267 2,162 13,479 12,584 10.1% 16.0% 1995 d 1,885 3,004 13,024 11,905 15.8% 23.1% 1996 d 1,444 2,681 11,563 10,326 14.0% 23.2% 1997 d 1,682 3,041 12,381 11,022 15.3% 24.6% 1997 e 1,402 2,670 15,938 14,670 9.6% 16.8% 1998 e 1,502 2,507 14,231 13,226 11.4% 17.6% 1999 e 1,521 2,354 15,179 14,346 10.6% 15.5% 2000 e 1,672 2,372 14,297 13,597 12.3% 16.6% 2001 e 1,466 2,071 13,833 13,228 11.1% 15.0% Total Percent Change 1993-2001 46.5% 17.0% -3.0% 21.8% Average Annual Percent Change 4.3% 1.8% -0.3% 2.2%

aCalculated by EPA as sh ipments + imports - exports. b Calculated by EPA as imports divi ded by implied dom estic consumption.

c Calculated by EPA as exports divided by shipments. d As no Census Trade data is available before 1997, Export and Import values are taken from USGS Mineral Yearbooks for years 199 3- 1997. “Metals and Alloys, Crude” represent SIC 3334 and “Plate , Sheets, Bars, Strip, etc.” is equivalent to SIC 3353.

eBefore 1998, the Department of Commerce co mpiled data in the SIC system; since 1998, th ese data have been compiled in the Nort h American Industry Classification System (NAI CS). For this analysis, EPA converted th e NAICS classification data to the SIC cod e classifications using the 1997 Economic Census Bridge Between NAICS and SIC

. Source: ASM 1997-2001 USGS 1993c-1997c.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-24 Table B2E-12 shows trends in exports and imports separately for aluminum metal and alloys and for semifinished products separately. U.S. aluminum companies ha ve a large overseas presence, which makes it difficult to analyze import data. Reported import data may reflect sh ipments from an overseas facility owned by a U.S. firm. The import data therefore do not provide a completely accurate picture of the extent to which foreign companies have penetrated the domestic market for aluminum. This table shows that imports have grown substantially in both categories between 1993 and 2003. Exports of prim ary aluminum have generally declined, with some fluctuation over the period. Exports to semifinished al uminum rose steadily until 1999, where they peaked for the period, and have since declined.

Table B2E-12: Trade Statistics for Aluminum and Semifabricated Aluminum Products (Quantities in thousand metric tons; Values in $millions)

Year Metals and Alloys, Crude Plate, Sheets, Bars, Strip, etc. Import a Export b Import a Export b Quantity Value Quantity Value Quantity Value Quantity Value 1993 1,840 2,150 400 541 400 837 594 1,481 1994 2,480 3,480 339 536 507 1,082 719 1,847 1995 1,930 3,690 369 690 622 1,643 812 2,619 1996 1,910 3,040 417 682 498 1,283 760 2,382 1997 2,060 3,500 352 606 562 1,519 882 2,746 1998 2,400 3,660 265 449 649 1,715 893 2,723 1999 2,650 3,760 318 515 735 1,777 907 2,564 2000 2,490 4,030 273 468 791 2,088 845 2,380 2001 2,560 3,930 192 320 683 1,762 751 2,120 2002 2,790 4,040 206 337 796 1,922 706 1,880 Total Percent Change 1993-2002 51.6% 87.9% -48.5% -37. 7% 99.0% 129.6% 18.9% 26.9% Average Annual

Growth Rate 4.7% 7.3% -7.1% -5.1% 7.9% 9.7% 1.9% 2.7%
Source: USGS 1994c-2002c.

a Table 10: U.S. Imports for Cons umption of Aluminum, by Class b Table 9: U.S. Exports of Aluminum, by Class



### B2E-4 F INANCIAL 

### C ONDITION AND 

### P ERFORMANCE The financial performance and condition of the aluminum industry are important determinants of its ability to withstand the costs of regulatory compliance without material adverse economic/financial impact. To provide insight into the industry’s financial performance and c ondition, EPA reviewed two key measures of financial performance over the 12-year period, 1992-2003: net profit margin and return on total capital. EPA calculated these measures as a revenue-weighted index of measure values for public reporting firms in the respective industries, using data from the Value Line Investment Survey. Financial performance in the most recent financial reporting period (2003) is obviously not a perfect indicator of conditions at the time of regulatory compliance. However, examining the trend, and deviation from th e trend, through the most recent reporting period gives insight into where the industry may be , in terms of financial performance and condition, at the time of compliance. In addition, the volatility of performance ag ainst the trend, in itself, provides a measure of the§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-25 potential risk faced by the industry in a future period in which compliance requirements are faced: all else equal, the more volatile the historical performance, the more likely the industry maybe in a period of relatively weak financial conditions at the time of compliance. Net profit margin is calculated as after-tax income be fore nonrecurring gains and losses as a percentage of sales or revenues, and measures profitability, as reflected in th e conventional accounting concept of net income. Over time, the firms in an industry, and the industry collectivel y, must generate a sufficient positive profit margin if the industry is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from a several factors, including: variations in aggregat e economic conditions (incl uding international and U.S. conditions), variations in industry-specific market c onditions (e.g., short-term capacity expansion resulting in overcapacity), or changes in the pricing and availabilityof inputs to the industry’s production processes (e.g., the cost of energy to the aluminum production process). The extent to which these fluctuations affect an industry’s profitability, in turn, depends heavily on the fixed vs. vari able cost structure of the industry’s operations. In a capital intensive industry such as the aluminum industry, the relatively high fixed capital costs as well as other fixed overhead outlays, can cause even small fluctuations in output or prices to have a large positive or negative affect on profit margin. Return on total capital is calculated as annual net profit, plus one-half of annual long-term interest, divided by the total of shareholders' equity and long- term debt (total capital). This concept measures the total productivity of the capital deployed by a firm or industry, regardless of the financial source of the capital (i.e., equity, debt, or liability element). As such, the return on total capital provides insight into the profitability of a business’ assets independent of financial structure and is thus a “purer” indicator of asset profitability than return on equity. In the same way as described for net profit margin , the firms in an industry, and the industry collectively, must generate over time a sufficient return on capital if the industry is to remain economically viable and attract capital. The factors causing short-term variation in net profit margin will also be the primary sources of short-term variation in return on total capital

. Figure B2E-7 below shows net profit margin and returnon total capital for the aluminum industry between 1992 and 2003. The graph shows considerable volatility . Performance was very low between 1988 and 1993, reflecting general economic weaknesses and oversupply in the market (McGraw-Hill, 2000). By the mid-1990s, performance improved as demand recovered and aluminum prices increased. Performance declined again though in 2000 through 2002, reflecting economic downturn in both the U.S. and world economies. By 2003, financial performance began to level off compared to the signifi cant declines experienced inthe three prior years. Improving financial performance on a quarter-to-quarter basis over the course of 2003 suggests that the Aluminum industry is in position for improving financial performance in 2004 and beyond as U.S. economic conditions continue to strengthen.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

4 Data on cooling water use are from the 1982 Census of Manufactures . 1982 was the last year in which the Census of Manufactures reported cooling water use.

5 The proposed Phase III regulation also applies to existing electric generating facilities as well as certain facilities in the oil and gas extraction industry and the seafood processing i ndustry. See Chapters B4 and B5 and Part C of this document for more informati on on these industries.

B2E-26 -2% 0% 2% 4% 6% 8% 10% 12% 14%

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Net Profit M argin - A luminum Indus try Return on Total Capital - A luminum Indus try Figure B2E-7: Net Profit Margin and Return on Total Capital for the Aluminum Industry

Source: Value Line, 1992-2003.



### B2E-5 F ACILITIES 

### O PERATING 

### C OOLING 

### W ATER 

### I NTAKE 

### S TRUCTURES Section 316(b) of the Clean Water Act applies to point s ource facilities that use or propose to use a cooling water intake structure and that withdraws cooling water dir ectly from a surface waterbody of the United States. In 1982, the Primary Metals industries as a whole (incl uding Steel and Non-ferrous producers) withdrew 1,312 billion gallons of cooling water, accounting for approximately 1.7 percent of total industrial cooling water intake in the United States 4 . The industry ranked 3rd in industrial c ooling water use, behind the electric power generation industry, and the chemical industry (1982 Census of Manufactures). This section provides information for facilities in the profiled aluminum segments potentially subject to the proposed regulation. Existing facilities that meet all of the following conditions are potentially subject to the proposed regulation: 5

< Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or arrangement with an independent supp lier who has a cooling water intake structure; or their cooling water intake structure(s) withdraw(s) cooling water from waters of the U.S., and at least twenty-five (25) percent of the water withdrawn is used for contact or non-contact cooling purposes;

< Have an National Pollutant Discharge Elimination Sy stem (NPDES) permit or are required to obtain one; and

< Have a design intake flow of greater than 2 million gallons per day (MGD). The proposed regulation also covers substantial additions or modificati ons to operations undertaken at such facilities. While all facilities that meet these criteria ar e subject to the regulation, this section focuses on the 21§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

6 EPA applied sample weights to the sampled facilities to account for non-sampled facilities and facilities that did not respo nd to the survey. For more information on EPA’s 2000 Section 316(b) Industry Survey, please refer to the Information Collection Request (U.S. EPA, 2000).

B2E-27 facilities nation-wide in the profiled aluminum segment s identified in EPA’s 2000 Section 316(b) Industry Survey as being potentially subject to this proposed regulation 6 . Information collected in the Detailed Industry Questionnaire found that an estimated 11 out of 31 Prim ary Aluminum producers (36 percent) and 10 out of 57 Aluminum Sheet, Plate, and Foil manufacturers (18 percent) meet the characteristics of a potential Phase III facility. B2E-5.1 Waterbody and Cooling System Type Table B2E-13 shows the distribution of potential Phase III facilities in the profiled aluminum segment by type of water body and cooling system. The table shows that three-quarters of the potential Phase III facilities use a once-through cooling system (16, or 77%) and one-quarter u se a recirculating system (5, or 23%). Ten of the 11 Section 316(b) Primary Aluminum producers obtain their cooling water from a freshwater stream or river. The other Section 316(b) Primary Aluminum producer draws fro m a lake or reservoir. Seven of the Section 316(b) Aluminum Sheet, Plate, and Foil manufacturers obtain their cooling water from either a freshwater stream or river. The other three Section 316(b) Aluminum Sheet, Plate, and Foil manufacturers draw from one of the Great Lakes. Seventy-six percent (16 facilities) of all Secti on 316(b) aluminum facilities withdraw their cooling water from a freshwater stream or river. None of the fac ilities withdraw from an estuary, the most sensitive type of waterbody.

Table B2E-13: Number of Potential Phase III facilities by Water Body Type and Cooling System Type for th e Profiled Aluminum Segments

Water Body Type Cooling System Recirculating Once-Through Total Number % of Total Number % of Total

Primary Production of Aluminum Freshwater Stream or River 0 0% 10 100% 10 Lake or Reservoir 1 100% 0 0% 1 Total 1 9% 10 91% 11 Aluminum Sheet, Plate, and Foil Freshwater Stream or River 3 43% 3 47% 7 Great Lake 0 0% 3 100% 3 Total 3 30% 7 70% 10 Total for Profiled Aluminum Facilities Freshwater Stream or River 3 18% 13 82% 16 Lake or Reservoir 1 100% 0 0% 1 Great Lake 0 0% 3 100% 3 Total 5 24% 16 78% 21 Source: U.S. EPA, 2000.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-28 0 0 7

3 5 7

0 1 2 3 4 5 6 7 8 9 10

<500 500-999 >=1000 Primary Production of Aluminum (SIC 3334) Aluminum Sheet, Plate, and Foil (SIC 3353) B2E-5.2 Facility Size Section 316(b) sample aluminum facilities are larger than facilities in their industries as a whole, as reported in the Census and discussed previously:

< Sixty-five percent of all facilities in the aluminum segment had fewer than 500 employees in 1992; none of the potential Phase III facilities in that segment fall into that employment category. Figure B2E-8 shows the number of potential Phase III facilities by employment size category.

Figure B2E-8: Number of Potential Phase III fac ilities by Employment Size for the Profiled Aluminum Segments

Source: U.S. EPA, 2000.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-29 B2E-5.3 Firm Size EPA used the Small Business Administration (SBA) sm all entity size standards to determine the number of existing Section 316(b) profiled aluminum industry fac ilities owned by small firms. Firms in the Primary Production of Aluminum segment are defined as small if they have 1000 or fewer employees; firms in theAluminum Sheet, Plate, and Foil segment are defined as small if they have 750 or fewer employees. Table B2E14 shows that three (or 27 percent) of the Section 316(b) Primary Aluminum producers are owned by small firms. The remaining eight (or 73 percent) Primary Aluminum producers are owned by large firms. All of the Section 316(b) Aluminum Sheet, Plate, and Foil producers are owned by large firms.

Table B2E-14: Number of Potential Phase III fa cilities by Firm Size for the Profiled Aluminum Segments

SIC Code Large Small Total Number % of SIC Number % of SIC 3334 8 73% 3 27% 11 3353 10 100% 0 0% 10 Total 18 86% 3 14% 21 Source: U.S. EPA, 2000; U.S. SBA, 2000; D&B, 2001.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-30 

### REFERENCES The Aluminum Association. Undated. Aluminum: An American Industry in Profile. The Aluminum Association. 2001. The Aluminum Situation. September 2001. The Aluminum Association. 1999. “Northwest Smelter Restarts Are Seen Unlikely”, Industry News. October 29, 1999. Bureau of Labor Statistics (BLS). 2002. Producer Price Index . Series: PCU33__#-Primary Metal Industries. Dun and Bradstreet (D&B). 2001. Data extracted from D&B Webspectrum August 2001. Executive Office of the President. 1987. Office of Management and Budget. Standard Industrial Classification Manual. Federal Reserve Board. 2004. Industrial Production and Capacity Utilization . Data extracted May 11, 2004. McGraw-Hill and U.S. Department of Commerce, International Trade Administration. 2000. U.S. Industry & Trade Outlook ‘00. McGraw-Hill and U.S. Department of Commerce, International Trade Administration. 1999. U.S. Industry & Trade Outlook ‘99. Standard & Poor’s (S&P). 2000. Industry Surveys - Metals: Industrial . January 20, 2000. Standard & Poor’s (S&P). 2001a. Industry Surveys - Metals: Industrial . July 12, 2001. Standard & Poor’s (S&P). 2004. Sub Industry Outlook- Alcoa Inc. . February 21, 2004. U.S. Department of Commerce (U.S. DOC). 1989-2002. Bureau of the Census. Current Industrial Reports. Survey of Plant Capacity

. U.S. Department of Commerce (U.S. DOC). 1988- 1991, 1993-1996, and 1998-2001. Bureau of the Census. Annual Survey of Manufactures. U.S. Department of Commerce (U.S. DOC). 1987, 1992, and 1997. Bureau of the Census. Census of Manufactures

. U.S. Department of Commerce (U.S. DOC). 1997. Bureau of the Census. 1997 Economic Census Bridge Between NAICS and SIC. U.S. Environmental Protection Agency (U.S. EPA). 2000. Detailed Industry Questionnaire: Phase II Cooling Water Intake Structures

. U.S. Environmental Protection Agency (U.S. EPA). 1995. Office of Enforcement and Compliance Assurance. Profile of the Nonferrous Metals Industry , EPA Office of Compliance Segment Notebook Project. EPA 310-R95-010. September, 1995. United States Geological Survey (USGS). 1996a-2004a. Mineral Commodity Summaries . Aluminum. Author: Patricia Plunkert. United States Geological Survey (USGS). 2001b-2003b. Mineral Industry Surveys. Aluminum. Author: Patricia Plunkert.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-31 United States Geological Survey (USGS). 1993c-2002c. Minerals Yearbook. Aluminum. Author: Patricia Plunkert. United States Geological Survey (USGS). 2002d. Historical Statistics for Mineral and Material Commodities in the United States. Aluminum. Authors: United States Geological Survey (USGS). 1998e. Metal Prices in the United States through 1998. U.S. Small Business Administration. 1989-2001. Statistics of U.S. Businesses

. U.S. Small Business Administration (U.S. SBA). 2000. Small Business Size Standards . 13 CFR section 121.201. Value Line. 1992-2003. Value Line Investment Survey

. Value Line. 2001. Metals & Mining (Diversified) Industry . July 27, 2001. Value Line. 2003a. Metal Fabricating Industry . March 28, 2003. Value Line. 2003b. Metals & Mining (Diversified) Industry . April 25, 2003.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2E: Aluminum

B2E-32 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Anal ysis for Existing Facilities B2F: Facilities in Other Industries

B2F-1 

### C HAPTER 

### C ONTENTS B2F-1 Facilities Operating Cooling Water Intake Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2F-2 B2F-1.1 Waterbody and Cooling System Types . . . . B2F-3 B2F-1.2 Facility Size . . . . . . . . . . . . . . . . . . . . . . . . B2F-3 B2F-1.3 Firm Size . . . . . . . . . . . . . . . . . . . . . . . . . . . B2F-4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B2F-5 

# Chapter B2F: Facilities in Other Industries (Various SICs)
The preceding profile sections focus on the five Primary Manufacturing Industries – Paper and Allied Products, Chemicals and Allied Products, Petroleum and Coal Products, Steel, and Aluminum – identified, after electric power generators, as using the largest amount of cooling water in their operations and most likely, after electric power generators, to be subject to the proposed regulation. However, facilities in other industries use cooling water and would therefore also be subject to the proposed regulation if they meet the regulation’s specifications. This section of the profile provides information on a sample of facilities in these Other Industries. Although EPA targeted its Detailed Industry Questionnaireat the electric power industry and the five Primary Manufacturing Industries, the Agency received 22 questionnaire responses from facilities with business operations in industries other than these major cooling water-intensive industries. EPA originally believed these facilities to be non-utility electric power generators; howev er, inspection of their responses indicated that the facilities were better understood as coo ling water-dependent facilities whose principal operations lie in businesses other than the electric power industry or the Primary Manufacturing Industries. Unlike the sample facility observations for the five Primary Manufacturing Industri es, the sample of observations from Other Industries is not based on a scientifically framed sample and the information from this sample of observations may not be reliably extrapolated beyond these facilities. As a result, EPA’s profile of information for the Other Industries facilities is restricted to these 22 sample facilities and is not presented as national estimates. The 22 Other Industries facilities fall in a wide range of businesses, as defined by 2-digit SIC industry group. Table B2F-1, following page, presents the number of r esponses received from facilities in the Other Industries by industry group.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Anal ysis for Existing Facilities B2F: Facilities in Other Industries

1 The proposed Phase III regulation also applies to existing electric generating facilities as well as certain facilities in the oil and gas extraction industry and the seafood processing industry. See Chapters B4 and B5 and Part C of this document for more information on these industries.

B2F-2 Table B2F-1: Facility Observations in Other Industries by 2-digit SIC code
No. of Facilities SIC Code SIC Description Important Operations

1 01 Agriculture production - crops Crops, plants, vines, and trees (excludi ng forestry operations); sod farms, and cranberry bogs; mushrooms, bulbs, flower seeds, and vegetable seeds; and growing of hydroponic crops.
3 10 Metal mining Mining, developing mines, or exploring fo r metallic minerals (ores); ore dressing and beneficiating operations, whether perfo rmed at mills operated in conjunction with the mines served or at mills, such as custom mills, operated separately.

1 14 Mining and quarrying of nonmetallic minerals, except fuels Mining or quarrying, developing mines, or exploring for nonmetallic minerals, except fuels; certain well and brine operations, and primary preparation plants, such as those engaged in crushing, gri nding, washing, or other concentration

12 20 Food and kindred products Manufacturing or processing foods and be verages for human consumption, and certain related products, such as manufact ured ice, chewing gum, vegetable and animal fats and oils, and prepared feeds for animals and fowls.

1 22 Textile mill products Preparation of fiber and subsequent manuf acturing of yarn, thread, braids, twine, and cordage; manufacturing broadwoven fabrics, narrow woven fabrics, knit fabrics, and carpets and rugs from yarn; dyeing and finishing fiber, yarn, fabrics, and knit apparel; coating, waterproofing, or otherwise treating fabrics; integrated manufacture of knit apparel and other fi nished articles from yarn; manufacture of felt goods, lace goods, non-woven fabric s, and miscellaneous textiles.

2 24 Lumber and wood products, except furniture Cutting timber and pulpwood; merchant sawmills, lath mills, shingle mills, cooperage stock mills, planing mills, and plywood mills and veneer mills engaged in producing lumber and wood ba sic materials; manufacturing finished articles made entirely or mainlyof wood or related materials.

1 34 Fabricated metal products, except machinery and transportation equipment Ferrous and nonferrous metal products, su ch as metal cans, tinware, handtools, cutlery, general hardware, nonelectric heating apparatus, fabricated structural metal products, metal forgings, metal st ampings, ordnance (except vehicles and guided missiles), and a variety of me tal and wire products, not elsewhere classified. 1 37 Transportation equipment Equipment for transportation of passengers and cargo by land, air, and water. Source: U.S. EPA, 2000; Executive O ffice of the President, 1987.



### B2F-1 F ACILITIES 

### O PERATING 

### C OOLING 

### W ATER 

### I NTAKE 

### S TRUCTURES Section 316(b) of the Clean Water Act applies to point s ource facilities that use or propose to use a cooling water intake structure and that withdraws cooling water dir ectly from a surface waterbody of the United States. This section provides information for facilities in Other Industr ies potentially subject to the proposed regulation. The proposed regulation applies to existing facilities that meet all of the following conditions: 1

< Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or arrangement with an independent supp lier who has a cooling water intake structure; or their cooling water intake structure(s) withdraw(s) cooling water from waters of the U.S., and at least twenty-five (25) percent of the water withdrawn is used for contact or non-contact cooling purposes;§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Anal ysis for Existing Facilities B2F: Facilities in Other Industries

B2F-3 < Have an National Pollutant Discharge Elimination Sy stem (NPDES) permit or are required to obtain one; and

< Have a design intake flow of greater than 2 million gallons per day (MGD). The proposed regulation also covers substantial additions or modificati ons to operations undertaken at such facilities. While all facilities that meet these criteria are subject to the regulation, this section focuses on the 22 facilities nation-wide in Other Industries identified in EPA’s 2000 Section 316(b) Industry Survey as being potentially subject to this proposed regulation. B2F-1.1 Waterbody and Cooling System Types Table B2F-2 reports the distribution of the Other Industr ies facilities by type of water body and cooling system. The majority of these facilities have either a once-through system (10, or 46 percent) or recirculating system (7, or 33 percent). In addition, a majority of these facilities draw water from a freshwater stream or river (12, or 55 percent). Of the four facilities that withdraw from an estuary, the most sensitive type of waterbody, three use a once-through cooling system. Plants with once-through cooling water systems withdraw between 70 and 98 percent more water than those with recirculating systems.

Table B2F-2: Number of Sampled Faciliti es by Water Body and Cooling System Type for Facilities in Other Industries

Water Body Type Cooling System Recirculating Combination Once-Through Other Total a Number % of Total Number % of Total Number % of Total Number % of Total Other Industries Freshwater Stream/ River 6 50% 1 8% 3 25% 2 17% 12 Estuary/ Tidal River 1 25% 0 0% 3 75% 0 0% 4 Lake / Reservoir 0 0% 0 0% 1 100% 0 0% 1 Great Lake 0 0% 2 50% 2 50% 0 0% 4 Ocean 0 0% 0 0% 1 100% 0 0% 1 Total a 7 32% 3 14% 10 45% 2 9% 22

aIndividual numbers may not add up to total due to independent rounding. Source: U.S. EPA, 2000.
B2F-1.2 Facility Size Figure B2F-1 shows the employment size category for the 22 sampled facilities identified as having primary operations outside of the power generation and Primary Manufacturing Industries already profiled. Half of the sampled facilities have between 100 and 500 em ployees and five have over 1,000 employees.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Anal ysis for Existing Facilities B2F: Facilities in Other Industries

B2F-4 2 5 8

3 4

0 1 2 3 4 5 6 7 8

<100 100-249 250-499 500-999 >=1000 Other Industries Figure B2F-1: Number of Sampled Facilities in Other Industries by Employment Size

Source: U.S. EPA, 2000.

B2F-1.3 Firm Size EPA used the Small Business Administration (SBA) sm all entity size standards to determine the number of sampled facilities in Other Industries that are owned by small firms. Depending on their SIC code, firms are defined as small based on either revenues or number of employees. Table B2F-3 shows that Section 316(b) facilities in Other Industries are predominantly owned by large firms. Overall, 19 facilities (86 percent) are owned by large firms, and 3 facilities (14 percent) are owned by small firms.

Table B2F-3: Number of Sampled Section 316(b) Facilities in Other Industries by Firm Size Large Small Total Other Industries 19 3 22 Source: U.S. EPA, 2000; U.S. SBA, 2000; D&B, 2001.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Anal ysis for Existing Facilities B2F: Facilities in Other Industries

B2F-5 

### REFERENCES Dun and Bradstreet (D&B). 2001. Data extracted from D&B Webspectrum August 2001. Executive Office of the President. 1987. Office of Management and Budget. Standard Industrial Classification Manual. U.S. Environmental Protection Agency (U.S. EPA) 2000. Detailed Industry Questionnaire: Phase II Cooling Water Intake Structures

. U.S. Small Business Administration (U.S. SBA). 2000. Small Business Size Standards . 13 CFR section 121.201.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Anal ysis for Existing Facilities B2F: Facilities in Other Industries

B2F-6 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2: Glossary

B2Glos-1 

### G LOSSARY Capital expenditures:As reported in the Economic Censuses, reflects permanent additions and major alterations, as well as replacements and additions to capac ity, for which depreciation, depletion, or Office of Minerals Exploration accounts are ordinarily maintain ed. Reported capital expenditures include work done on contract, as well as by the mine forces. Totals for expenditures include the costs of assets leased from other concerns through capital leases. Excluded are expenditures for land and cost of maintenance and repairs charged as current operating expenses. Also excluded are capita l expenditures for mineral land and rights which are shown as a separate item. Capacity utilization:Indicates the extent to which plant capac ity is being used and shows potential excess or insufficient capacity. This profile reports capacity utilization as published by the U.S. Bureau of Census in the Survey of Plant Capacity published in the Current Industrial Reports. The utilization rate is equal to an output index divided by a capacity index. Output is measure d by seasonally adjusted indexes of industrial production, and is based on actual output in 1992. The capacity inde xes attempt to capture the concept of sustainable practical capacity, which is defined as the greatest level of output that a plant can maintain within the framework of a realistic work schedule, taking account of normal downtime, and assuming sufficient availability of inputs to operate the machinery and equipment in place. Concentration ratio:The combined percentage of total industry output accounted for by the largest producers in the industry. For example, the four-firm concentration ratio (CR4) refers to the market share of the four largest firms. The higher the concentration ratio, the more concentrated the industry. A market is generally considered highly concentrated if the CR4 is greater than 50 percent. Coverage ratio:The ratio of primary products shipped by the establishments classified in the industry to the total shipments of such products that are shipped by all manufacturing establishments, wherever classified. An industry with a high coverage ratio accounts for most of the value of shipments of its primary products, whereas an industry with a low coverage ratio produces a smaller portion of the total value of shipments of its primary products produced by all sources. Employment:Total number of full-time equivalent employees, including production workers and nonproduction workers. Export dependence:The share of shipments by domestic producers that is exported; calculated by dividing the value of exports by the value of domestic shipments. Herfindahl-Hirschman index (HHI):An alternative measure of concentr ation. Equal to the sum of the squares of the market shares for the largest 50 firms in th e industry. The higher the index, the more concentrated the industry. The Department of Justice uses the HHI for antitrust enforcement purposes. The benchmark used by DOJ is 1,000, where any industry with an HHI less than 1,000 is considered to be unconcentrated. The advantage of the HHI over the concentration ratio is th at the former gives information about the dispersion of market share among all the firms in the industry, not just the largest firms (Arnold, 1989). Import penetration:The share of all consumption in the U.S. that is provided by imports; calculated by dividing imports by reported or a pparent domestic consumption (the latter calculated as domestic value of shipments minus exports plus imports). Labor productivity:Amount of output produced per unit of labor inputon average. Calculated in this profile as real value added divided by production hours. This m easure indicates how an industry uses labor as an input in the production process. Changes over time in labor productiv ity may reflect changes in the relative use of labor versus other inputs to produce output, due to technological changes or cost-cutting efforts. Changing patterns of labor utilization relative to output are particularly im portant in understanding how regulatory requirements may translate into job losses, both in aggregate and at the community level.§ 316(b) Proposed Rule: Phase III – EA, Part B: Ec onomic Analysis for Existing Facilities B2: Glossary

B2Glos-2 Nominal values:Dollar values expressed in current dollars. Operating margin:Measure of the relationship between input co sts and the value of production, as an indicator of financial performance and condition. Everything else being equal, industries and firms with lower operating margins will generally have less flexibility to absorb the costs associated with a regulation than those with higher operating margins. Operating margins were calculated in this profile by subtracting the cost of materials and total payroll from the value of shipments. Operating margin is only an approximate measure of profitability, since it does not consider capital costs and other costs. It is u sed to examine trends in revenues compared with production costs within an industry; it should not be used fo r cross-industry comparisons of financial performance. Primary product shipments:An establishment is classified in a particular industry (4-digit SIC codes) if its shipments of the primary products of that industry exceedin value its shipments of the products of any other single industry. An establishment’s primary product sh ipments are those products considered primary to its industry. Producer production indexes (PPI):A family of indexes that measures the average change over time in selling prices received by domestic producers of goods and services (Bureau of Labor Statistics, PPI Overview). Used in this profile to convert nominal values into real 1997 dollar values. Real values:Nominal values normalized using a price index to express values in a single year’s dollars. Removes the effects of price inflation wh en evaluating trends in dollar measures. Secondary product shipments:An establishment’s products that are considered secondary to the industry in which the establishment is classified and primary to other industries. For example, a petroleum refinery classified in SIC code 2911 would produce petroleum products as pr imary products, but might produce organic chemicals as secondary products. Value added:A measure of manufacturing activity, derived by subtracting the cost of purchased inputs (materials, supplies, containers, fuel, purchased electric ity, contract work, and contract labor) from the value of shipments (products manufactured plus receipts for servi ces rendered), and adjusted by the addition of value added by merchandising operations (i.e ., the difference between the sales valu e and the cost of merchandise sold without further manufacture, processing, or assembly) plus the net change in finished goods and work-in-process between the beginning-and end-of-year inventories. Valu e added avoids the duplication in value of shipments as a measure of economic activity that results from the u se of products of some establishments as materials by others. Value added is considered to be the best value measure available for comparing the relative economic importance of manufacturing among industries and geographic areas. Value of shipments:Net selling values of all products shipped as well as miscellaneous receipts. Includes all items made by or for an establishments from materials owned by it, whether sold, transferred to other plants of the same company, or shipped on consignment. Value of sh ipments is a measure of the dollar value of production, and is often used as a proxy for revenues. This profile u ses value of shipments to indicate the size of a market and how the size differs from year to year, and to calculate operating margins.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

1 EPA applied sample weights to 199 sample facilities to acc ount for non-sampled facilities and facilities that did not respond to the survey. For more information on EPA’s 2000 Section 316(b) Industr y Survey, please refer to the Information Collection Request (U.S. EPA, 1999a).

B3-1 

# Chapter B3: Economic Impact Analysis for Manufacturers

I NTRODUCTION This chapter assesses the expected economic effect of the proposed section 316(b) Regulation for Phase III Facilities on the Manufacturers that would be subject to national categorical requirements under the proposed regulation. The analysis focuses on impacts in five key manufacturing industries – Paper, Chemicals, Petroleum, Aluminum, Steel (the “Primary Manufacturing Industries”) – in which a substantial number of facilitiesare expected to be subject to the proposed regulation. EPA’s analysis of the regulation’s expected impact in these industries is based on a statistically valid sample survey of facilities in these five industries. The sample survey indicates that the regulation would potentially subject as many as 532 facilities in the Primary Manufacturing Industries 1 to national requirements. This chapter also considers the effect of the regulation on facilities in other industries (“Other Industries”) that are expected to be within the scope of the regulation. The f acility impact analysis for Other Industries is restricted to a sample of 22 facilities for which EPA received surveys, but which are not part of the statistically valid sample. As a result, EPA’s analysis for the Other Industri es group is limited to these known facilities. EPA has not estimated the number of facilities in the Other Industries group that may be subject to the regulation because EPA does not believe that this number can be reliably extra polated from the sample of known facilities in this group. However, because the statistically valid survey group of six industries (i.e., for the five Primary Manufacturing Industries and Electric Generators) reflects 99% of tota l cooling water withdrawals, EPA believes that few additional facilities in the Other Industries group are potentially subject to the proposed regulation. Although EPA was able to undertake impact analysis for the Other Industries group using only the sample of known facilities for this group, EPA believes that its anal ysis for the Other Industries group provides a sufficient basis for regulation development. EPA’s review of the e ngineering characteristics of cooling water intake and use in the Other Industries group indicates that cooling water intake and use in these industries do not differ materially from cooling water intake and use in th e electric power industry and the Primary Manufacturing Industries. In addition, EPA specifically analyzed th e economic impacts of the three proposed options on the 22 sample facilities in the Other Industries group and found no economic impact of the proposed options on these facilities. For these reasons, EPA believes that its findi ngs of no economic impact to the known facilities in the Other Industries group, and thus the practicability of the three proposed options, are generally applicable to the full breadth of industries, including the Other Industries group, within the regulation’s scope. Based on the sum of the sample-weighted estimate of 532 facilities in the Primary Manufacturing Industries and the 22 known facilities in the Other Industries group, EPA included a total of 554 potentially regulated facilities 

### C HAPTER 

### C ONTENTS B3-1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . B3-3 B3-2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . B3-3 B3-2.1 Market-Level Impacts . . . . . . . . . . B3-5 B3-2.2 Impact Measures . . . . . . . . . . . . . . B3-5 B3-3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-15 B3-3.1 Baseline Closures . . . . . . . . . . . . B3-15 B3-3.2 Number of Facilities with Regulatory Requirements . . . . . . . . . . . . . . . . B3-16 B3-3.3 Post-Compliance Impacts . . . . . . B3-17 B3-3.4 Compliance Costs . . . . . . . . . . . . B3-17 B3-3.5 Summary of Facility Impacts . . . B3-18 B3-3.6 Firm Impacts . . . . . . . . . . . . . . . . B3-19 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-21 Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-22 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3-23 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A-i§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-2 in the economic impact analysis for the Manufacturer s segment. The total number of Manufacturers segment facilities considered in the economic impact analysis ( 554) differs from the number of facilities potentially subject to regulation (566), as reported in Chapter A1. EPA dete rmined that the survey responses of 14 sample facilities lacked certain financial data needed for the facility im pact analysis while containing sufficient data to support estimates of facility counts and compliance costs. EPAtherefore retained these sample facilities (37 sample weighted facilities) in the analyses to estimate the total number of Manufacturers facility potentially subject to regulation but excluded them from the economic impact analysis. When these sample facilities were excluded from the impact analysis, the sample weights for rema ining facilities within the affected sample frames were adjusted upwards to account for their removal. The diffe rence in the reported facility totals in the impact and social cost analyses reflects the removal of these 14 f acilities and the use of adjusted sample weights. The removal of specific sample facilities from the analysis universe and simultaneous adjustment of sample weights to account for their removal yields the same estimate of the total combined population of Manufacturers and Electric Generators for the analysis. However, as a result of the sample stratification methodology, the estimates of the total facility populations for Manufacturers onlydiffer slightly between the two sample facility cases. Both values are valid statistical estimates of the same, but unknown, value of the Manufacturers facility population. EPA undertook the economic impact analysis to aid in assessing the economic achievability of alternative regulatory options and, on the basis of that assessment, to aid in defining the proposed regulation. Measures of economic impact include facility closures and associated losses in employment, financial stress short of closure (“moderate impacts”), and firm-level impacts. Severe impactsare facility closures and the associated losses in jobs at facilities that close due to the regulation. EPA also assessed moderate economic impacts to support its evaluation of regulatory options and to understa nd better the regulation's economic impacts. Moderate impacts are adverse changes in a facility's financial position that are not threatening to its short-term viability. The firm impact analysis assesses whether firms that own multiple facilities are likely to incur more significant impacts than indicated by the facility impact analysis. Impacts may be more significant at the firm level than at the facility level if a firm owns a number of facilities that incur significant cost. In addition, a firm-level analysis is needed to assess impacts on small businesses, as required bythe Regulatory Flexibility Act and SBREFA. Other chapters consider the impacts on small entities ( Chapter D1: Regulatory Flexibility Analysis ) and impacts on governments ( Chapter D2: UMRA Analysis ). This chapter presents the impact analysis results for the three proposed options: the “50 MGD for All Waterbodies” option (“50 MGD All”), the “200 MGD fo r All Waterbodies” option (“200 MGD All”), and the “100 MGD for Certain Waterbodies” Option (“100 MGD CWB”). These options differ with regard to (1) their design intake flow (DIF) applicability thresholds (50, 100, and 200 MGD, respectively); and (2) the type of waterbodies to which they would apply (the options with the 50 and 200 MGD applicability thresholds would apply to all waterbody types while the 100 MGD app licability threshold option would apply only to certain waterbody types – an ocean, estuary, tidal river/stream, or one of the Great Lakes). Facilities meeting these applicability criteria would be required to meet simila r requirements to those required in the final Phase II regulation, including a 80-95% reducti on in impingement mortality and a 60-90% reduction in entrainment. Facilities not meeting these criteria would continue to be subject to 316(b) requirements established by permit writers based on their Best Professional Judgment (BPJ). As a result, the number of facilities required to meet the national categorical requirements would vary under each of the three proposed options. Of the three options presented here, the 100 MGD for Certain Waterbodies Opti on would subject the smallest number of facilities to national categorical requirements, with the 200 MGD for All Waterbodies Option and 50 MGD for All Waterbodies Option subjecting successively larger num bers of facilities to national requirements. As outlined in Chapter A1: Introduction , EPA considered several additional regulatory options based on varying flow regimes and waterbody types, in arriving at the proposed options. Summary results for five additional options can be found in Appendix 1 to this chapter. This chapter describes the methodologyused to assess economic impacts for the Manufacturers facilities, and presents the results of the analyses.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

2 See Appendix 2 to Chapter B3 for details of the downtime cost calculation.

3 The annualization approach used for the f acility impact analysis differs from that us ed to develop the social cost estimate p resented in Chapter B1: Summary of Cost Categories and K ey Analysis Elements for Existing Facilities . For the analysis of the social cost of the proposed regulation, the present value of total cost and the cons tant annual equivalent to that present value (annualized cost) were calculated as of the expected effectiveness date of the fina l regulation for Phase III facilities, beginning of year 2007. In contrast, for the impact analysis, the present value and annualized value of compliance cost were determined as of the fi rst year of compliance o f each facility (for this analysis, assumed to be 2010 to 2014).

B3-3 

### B3-1 D ATA 

### S OURCES The economic impact analyses rely on data provided in the financial portion of the detailed questionnaires distributed by EPA to facilities potentially subject to the Phase III regulation under the authority of Section 308 of the Clean Water Act. The survey financial data included facility and parent firm income statements and balance sheets for the three years 1996, 1997, and 1998. In addition to the survey data, a number of secondary s ources were used to characterize economic and financial conditions in the industries subject to the proposed Phase III regulation. Secondary sources used in the analyses include:

< Department of Commerce economic census and survey data, including the Census of Manufactures, Annual Surveys of Manufactures, and international trade data;

< U.S. Industry and Trade Outlook, published by McGraw-Hill and the U.S. Department of Commerce;

< Value Line Investment Survey;

< Annual Statement Studies , published by Risk Management Association (RMA); and

< Statistics of U.S. Businesses (SUSB).



### B3-2 M ETHODOLOGY The impact analysis starts with compliance cost estimat es from the EPA engineering analysis and then calculates how these compliance costs would affect the financial c ondition of Section 316(b) Manufacturers. EPA included the following compliance cost categories in this analysis: capital cost, annual operating and maintenance cost, administrative cost, and the lo ss of business income from potential shutdown of facilities during installation of compliance equipment2 . Of these cost categories, only operating and maintenance and certain administrative costs recur annually. The remaining costs occur only once at the beginning of compliance or on a multi-year interval over the period of the compliance analysis. So me of the impact analyses require combining the annually recurring and non-recurring costs into a single, annual equi valent value. For combining the annually recurring and non-recurring costs in this analysis, EPA calculate d the annual equivalent cost of the non-recurring cost categories and added these annualized costs to the annually recurring operating and maintenance cost. To derive the constant annual valu e of the non-annual costs, EPA annualized each cost component over the component’s estimated useful life, using a 7.0% discount rate. The cost of compliance equipment, which includes fine-mesh traveling screens, with and without fish handli ng, and fish handling and return systems, was annualized over 10 years; initial permitting cost and the income loss from installation shutdown were annualized over 30 years; and repermitting cost was annualized over 5 years 3 . For more information on the compliance cost components developed for this analysis, see Chapter B1: Summary of Cost Categories and Key Analysis Elements for Existing Facilities and the § 316(b) Technical Development Document (U.S. EPA, 2004).§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-4 As discussed in Chapter B1: Summary of Cost Categories and Key Analysis Elements for Existing Facilities , the various economic information used in this analysis were initially provided in dollars of different years. For example, facility financial data obtained in the 316(b) survey for Manufacturers are for the years 1996, 1997, and 1998, while the technology costs of regulatory compliance we re estimated in dollars of the year 2002. To support a consistent analysis using these data that were initially developed in dollars of different years, EPA needed to bring the dollar values to a common analysis year. For th is analysis, EPA adjusted all dollar values to constant dollars of the year 2003 (average or mid-year, dependi ng on data availability) using an appropriate inflation adjustment index. For adjusti ng compliance costs, EPA used the Construction Cost Index (CCI) published by the Engineering News-Record. For financ ial statement information, EPA used the Gross Domestic Product Implicit Price Deflator (GDP Deflator) to bring dollar values to 2003. The values used to adjust the dollar values to constant dollars can be found in Chapter B1. For the impact analysis, EPA first eliminated from an alysis those facilities showing materially inadequate financial performance in the baseline, that is, in the absence of the regulation. EPA judged these facilities, which are referred to as baseline closures , to be at substantial risk of financ ial failure regardless of any financial impacts of the 316(b) regulation. Second, for the re maining facilities, EPA evaluated how compliance costs would likely affect facility financial health. A facility is identified as a regulatory closureif it would have operated under baseline conditions but would fall below an acceptable financial performance level when subject to the new regulatory requirements. EPA’s analysis also identified facilities that would lik ely incur moderate impacts from compliance with the regulation. EPA anticipates that these facilities would expe rience moderate deterioration of financial performance but not at a level sufficient to cause the facility to fail fi nancially. The test of moderate impacts examined two financial ratios – pre-tax return on assets and inter est coverage ratio – calculated on a baseline and post-compliance basis. Incremental moderate impacts ar e attributed to the regulation if both financial ratios exceeded threshold values in the baseline (i.e., no moderate impacts in the baseline), but at least one financial ratio fell below the threshold value in the post-compliance case. For the assessment of firm-level effects, EPA compared annualized after-tax compliance cost to firm revenue and reports the estimated number and percentage of firms in curring compliance cost in three cost-to-revenue ranges: less than 1.0%; at least 1.0% but less than 3.0%; and 3.0% or greater. Although EPA’s sample-based data support specific estimates of the number of facilities, these data do not support a specific estimate of the number of entities that own these facilities. As a result, EPA es timated the number of entities owning facilities in the manufacturing industries as a range, based on alternative assumptions about the potential ownership of regulated facilities. In its comparison of compliance cost to firm revenue, EPA also used this same range concept, which yields approximate upper and lower bound estimates of the va lue of compliance cost that might be incurred by an entity, based on the number of regulated facilities that it owns. Key steps in the facility- and firm-level impact me thodologies are described in the following discussions. In addition, seven appendixes to this chapter provide deta il of specific aspects of the impact analysis methodologies.
B3-2.1 Market-Level Impacts Increased cost from the regulation may affect industry- level prices and output. In some instances, facilities incurring compliance costs may be able to pass part of these costs through to customers as price increases and thus reduce the compliance cost burden borne directly by complying facilities. On the basis of analysis presented in Appendix 3 to Chapter B3 and the findings from th e industry profile analyses as discussed in the preceding chapter, Chapter B2: Profile of Manufacturers , EPA determined that an assumption of zero cost pass-through is appropriate for its analysis of the effect of the 316(b) regulation on manufacturing industries. The assumption of zero cost pass-through is conservative in that the anal ysis assumes that facilities must bear all compliance costs within baseline cash flow. Because facilities may be able to pass compliance costs through to consumers in some markets, this assumption may overstate impacts to aff ected facilities and understate the ability of facilities to withstand the cost of 316(b) regulatory compliance without material financial impact.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-5 B3-2.2 Impact Measures a. Test of Severe Impacts The analysis of severe impacts estimates the number of fac ilities that could potentially close due to the regulation. EPA predicted that a facility would close if compliance costs cause the facility's overall financial performance and resulting implied financial value to fa ll below a specified threshold level. The assessment of severe impacts for 316(b) manufacturing facilities is based on the change in the facility’s estimated business value, as determined from a discounted present value analysis of baseline cash flow and the change in cash flow resulting from regulatory compliance. If the estimated discounted cash flow value of the facility is positive before considering the effects of regulatory compliance but becomes negative as a result of compliance outlays, then the facility is considered a regulat ory closure. In this impact test, the estimated ongoing business value of the facility is compared with a threshol d value of zero for the closure decision: as long as the discounted cash flow value of the facility is greater thanzero, the business is earning its cost of invested capital and continuation of the business is warranted. If the discount ed cash flow value of the facility is less than zero in the baseline or becomes less than zer o as a result of compliance outlays, then the business would not earn its cost of invested capital and the business owners would be bette r off financially by terminating the business. As noted in earlier discussion, facilities for which EPA estimated a negative baseline value were considered baseline closures and were not tested for additional adverse impacts from regulatory compliance. In an alternative, theoretically more accurate, formulation of this concept, business owners would compare the discounted cash flow value of the facility with the value that the facility’s assets would bring in liquidation. In this case, the estimated ongoing business value would be comp ared with a value that may be different from zero: liquidation value could be positive or negative. When liqui dation value is positive, business owners might benefit financially by terminating a business and seeking its liquidation value even when the ongoing business value is positive but less than the estimated liquidation va lue. With negative liquidation value – which generally would result from business termination liabilities (e.g., site clean-up) – the opposite result could occur: business owners may find it financially advantageous to remain in business even though the business earns less than its cost of invested capital , if the liquidation value of the business is “mor e negative”, and thus less in value, than the ongoing business based on the discounted cash flow analysis . EPA attempted to implement this alternative impact test formulation. EPA judges that the liquidation value estimates are substantially speculative and subject to considerable error. For these reasons, EPA deci ded against using liquidation value for comparison with ongoing business value in the closure test. The cash flow concept used in calculating ongoi ng business value for the closure analysis is free cash flow available to all capital. Free cash flow is the cash availa ble to the providers of capital – both equity owners and creditors – on an after-tax basis from business operati ons, and takes into account the cash required for ongoing replacement of the facility’s capital equipment. Free cash flow is discounted at an estimated after-tax total cost of capital to yield the estimated business value of the facility . Details of the calculation of free cash flow and the discounting of free cash flow to yield the facility’s estimated value are explained in the following sections. 

###  ̃ Calculation of Baseline Free Cash Flow and Performance of Baseline Closure Test Calculation of baseline free cash flow and performance of the baseline closure test involved the following steps:
1. Average survey income statement data over res ponse years and convert to mid-year 2003 dollars: EPA first adjusted facility income statement data for 1996, 1997, and 1998 to the year 1998, using the GDP Deflator. These data were then averaged over the months and/or years for which survey respondents reported data to develop an annual average income statement in 1998 cons tant dollars. For example, if a facility reported income statement data for 1996, 1997, and 1998, then a simple average was calculated for the three reported years. The annual average income statement in1998 was then brought forward from 1998 to 2003, again using the GDP Deflator.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-6 2. Calculate after-tax income excluding the effects of financial structure: The questionnaire responses include a calculation of after-tax income in accord with conventional accounting principles. However, this calculation reflects the financial structure of the business, which may include debt financing and thus interest charges against income. Because the cash flow concept to be discounted in the business value analysis is cash flow available to all capital, it is necessary to restate after-tax income to exclude the effects of debt financing, or on a before-interest basis. This restatement involves: (1) incr easing after-tax income by the amount of interest charges and (2) increasing taxes (and thereby reducing after-tax income) by the amount of tax reduction provided by interest deductibility. This adjustment amount s to adding tax-adjusted interest expense to aftertax income and yields an estimate of after-tax income independent of capital structure or financing effects . In calculating the tax adjustment for interest, EPA used a co mbined federal/state corporate income tax rate. For this calculation, EPA used a tax rate that integrates the federal corporate income tax rate (35%) and statespecific state corporate income tax rates, based on facility location. The combined federal/state corporate in come tax rate was calculated as follows:

J = J S + J F - ( J S * J F ) (B3-1)
where: J = estimated combined federal-state tax rate; J S = state tax rate; and J F = federal tax rate (35%).
After-tax income, before interest, was calculated as follows:

ATI-BI = ATI + I - J I or ATI-BI = ATI + (1 - J )I (B3-2)

where: ATI -BI = after-tax income before interest ; ATI = after-tax income from baseline financial statement; I = interest charge from baseline financial statement; and J = estimated combined federal-state tax rate.
3. Calculate after-tax cash flow from operations, befo re interest, by adjusting income for non-cash charges: The calculation of after-tax income may include a non-cash charge for depreciation (and potentially amortization). To convert income to after-tax cash flow (ATCF) from operations, it is therefore necessary to add back any depreciation charge to the calculation of af ter-tax income, before interest. Cash flow, before interest , was calculated as follows:

ATCF -BI = ATI -BI +D (B3-3a)
where: ATCF -BI = after-tax cash flow before interest ; ATI -BI = after-tax income before interest ; and D = baseline depreciation. As a final step in the calculation of after-tax cash flow before interest, EPA eliminated the implied cash flow benefit of any negative taxes, as reported in the facility’s income statement and after adjustment for removal of interest. That is, in these calculations, negative taxes in crease after-tax income and cash flow, and thus appear to improve the financial performance and value of the fac ility in terms of cash flow from operations. However, whether and when the implied cash flow benefit of negative taxes can be realized depends on the overall§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-7 profitability and tax circumstances of the total enterpri se, including any other facilities owned by the same firm, and the extent of profitability in periods before or afte r the survey data periods. To be conservative in this analysis, EPA therefore assumed that a facility would not receive the implied cash flow benefit from negative taxes – negative taxes, after adjustment for interest, were set to zero in the baseline analysis. This assumption is consistent with a later step in the post-compliance anal ysis in which EPA limited the cash flow benefit of tax deductions on compliance outlays not to exceed the amount of taxes paid as reported in the baseline income statement (and adjusted for interest). In theory, the application of this limit could cause some facilities that would otherwise pass the baseline closure anal ysis, instead to fail the analysis if the reported amount of negative tax, after adjustment for interest, would be sufficient to o ffset the negative cash flow from operations independent of taxes. In practice, though this limitation did not affect the findings of the baseline closure analysis.
4. Adjust after-tax cash flow to reflect estimated real change in business performance from the time of survey data collection to the present: EPA adjusted facility baseline cash flowto reflect the estimated real change (i.e., independent of inflation) in business performan ce in the manufacturing industries from the time of the facility survey, 1996-1998, to the present. This adjustme nt is intended to address two potential concerns that could lead to biased findings from the regulatory impact assessment:

C First, EPA was concerned that facility survey data mi ght have been collected during a period that deviated cyclically from the longer-term trend of business performance for the 316(b) manufacturing industries. Given the knowledge that U.S. business conditions dur ing the latter half of the 1990s were cyclically strong, EPA was particularly concerned that business conditions during the 316(b) survey period (1996-1998) might be abnormally favorable for some of the five Primary Manufacturing Industries. In this case, the business performance and valuation m easures, based on survey data, used to assess the burden of regulatory compliance costs might overstate industry's ability to bear these costs and therefore understate the potential impact of the proposed regulation.

C Second, apart from the issue of short-term deviation from trend caused by a cyclically strong economy, EPA was also aware from its profile analyses that some of the industries might be experiencing a longer-term trend of deteriorating performance. Using sample facility data that don't reflect such possible trends would again potentially ove rstate industry's ability to bear compliance costs and therefore understate the potential impact of the proposed regulation. To calculate the adjustment factor, EPA collected data on after-tax cash flow for public firms in the 316(b) manufacturing industry sectors over a 12-year period and developed adjustment factors by industry and/or key industry segment (details of this analysis are cont ained in Appendix 4 to Chapter B3). Adjusted after-tax cash flow is calculated as follows:

ATCF -BI ADJ= ATCF -BI * Adj (B3-3b)
where: ATCF -BI ADJ = after-tax cash flow before interest adjusted to reflect the real change in business performance; a ATCF -BI = after-tax cash flow before interest ; and Adj = adjustment factor to reflect th e real change in business performance.
5. Calculate freecash flow by adjusting after-tax cash flow from operations for ongoing capital equipment outlays: The measure of after-tax cash flow from the pr evious step, cash flow from operations, reflects the cash receipts and outlays from ordinary business operati ons, but includes no allowance for replacement of the facility’s existing capital equipment. To sustai n ongoing operations, however, a business must expend cash for capital replacement. Accordingly, to understand th e true cash flow of a business and thus provide a conceptually valid cash flow measure for business valuation, it is necessary to reduce cash flow from operations by an allowance for capital replacement. For the calculation of free cash flow, EPA estimated baseline capital outlays from a regression analysis of capital expenditures by public firms in the 316(b)§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-8 industry sectors over an 11-year period (details of this analysis and estimation framework are contained in Appendix 5 to Chapter B3). Free cash flow is calculated as follows:

FCF = ATCF -BI ADJ- CAPEX (B3-3b)
where: FCF = free cash flow ATCF -BI ADJ= after-tax cash flow before interest adjusted to reflect the realchange in business performance; and CAPEX = estimated baseline capital outlays. Or on a more detailed accounting statement basis:

FCF = REV - TC - T - J I - CAPEX (B3-3c)
where: FCF = free cash flow REV = revenue TC = total operating costs, excluding interest, depreciation, and taxes T = baseline income tax J = estimated combined federal-state tax rate; I = interest charge from baseline financial statement; J I = the increase in tax liability resulting from calcu lating income on a pre-interest basis; and CAPEX = estimated annual baseline capital outlays. This calculation of free cash flow is based on a static representation of a facility’s business. With the exception of bringing estimated cash flow forward from the time of the survey, 1996-1998, to approximately the present, 2003, the facility impact analysis assumes, in effect, that the facility’s business will continue in the future – absent the effects of regulation – exactly as reflected in the baseline financial statements provided in the survey questionnaire
6. Calculate baseline facility value as the present value of free cash flow over a 10-year analysis horizon: To calculate baseline business value, EPA discounted free cashflow over a 10-year period at an estimated real (i.e., excluding the effects of inflation), after-tax co st of capital of 7.0%. The use of 10 years as the discounting horizon reflects the expected useful life of capital equipment to be installed for 316(b) regulation compliance. Facility baseline busin ess value is calculated as follows:

(B3-4)

where: VALUE = estimated baseline business value of the facility FCF = free cash flow CoC = after-tax cost-of-capital (7.0%); and t = year index, t = 0-9 (10-year discounting horizon). In the present value calculation, yearly cash flows accrue at the beginning of the year. As a result, the first year of cash flows is not discounted – i.e., t = 0 for the first year of the analysis – and cash flows in the tenth and final year of the analysis period are discounted over a 9-year period – i.e., t = 9 in the final year of the analysis.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

4 For the facility cash flow analysis, EP A treated the income loss from installation downtime on an annual equivalent basis eve n though this financial event occurs only once, and at the beginning of the assumed analysis period. EPA treated the installatio n downtime on an annualized basis for two reasons. First, the installation downtime is assumed to have a useful “financial life” of 30 ye ars to reflect the total potential business life of the facility (note that rein stallation of the basic capital equipment, which is assumed to recur on a 10-year interval, does not require a new round of downtime). Since comp liance capital equipment is assume d to have a 10-year useful l ife and the discounted cash flow analysis is accordingl y structured as a 10-year analysis, including the income loss from installation downtime (which is assumed to have a 30-year useful life) as a one-time up-front cost would overstateits impact in the discounted cash flow ca lculation. Second, calculation of the downtime cost on an annual basis allows the tax effect from the one-time income loss to be summed wi th other annual tax effects for applying the limit to tax offsets, as explained in the next step of the analysis.

B3-9 As explained above, EPA considered a facility to be a baseline closure if its estimated business value was negative before incurring regulatory compliance costs. Base line closures were neither tested for adverse impact in the post-compliance impact analysis nor were their compliance costs included in the tally of total costs of 316(b) regulatory compliance. 

###  ̃ Calculation of Post-Compliance Free Cash Flow and Performance of Post-Compliance Closure Test For the post-compliance closure analysis, EPA recalcu lated annual free cash flow, accounting for changes in annual expenses and taxes that are estimated to result from compliance-related outlays. EPA combined the postcompliance free cash flow value and the estimated comp liance capital outlay in the present value framework to calculate business value on a post-compliance basis. Calculation of post-compliance free cash flow and perform ance of the post-compliance closure test involved the following steps:
1. Adjust baseline annual free cash flow to reflect compliance expense effects: Compliance-related effects on annual free cash flow include: annually recurring operati ng and maintenance costs; the annual equivalent of permitting and repermitting costs, which recur on other than an annual basis over the life of the analysis; the annual equivalent of the income loss from installation downtime (see Appendix 2); and related changes in taxes 4 . The change in taxes includes: (1) the tax eff ect of these annually recurring and annualized expenses and (2) the tax effect from depreciation of initial co mpliance outlays. For calculating the tax effect of depreciation, EPA assumed that compliance capital outlays would be depreciated for tax purposes on a 10- year straight-line schedule. Post-compliance free cash flow was calculated as follows:

FCF PC = FCF BL- ) TC - J (- ) TC - ) D) (B3-5)
where: FCF PC = post-compliance free cash flow; FCF BL = baseline free cash flow, as calculated above; ) TC = change in total facility annual costs (excluding interest, depreciation and taxes), calculated as the cost of operating and maintaining compliance equi valent plus the annual equivalent of certain non-annual costs, as described above; J = marginal tax rate for calculating compliance-relate d tax effects (combined federal-state tax rate); and ) D = change in depreciation expense, calculated as compliance capital outlay (CC) divided by 10.
2. Limit tax adjustment to not exceed taxes as reported in baseline financial statement: The tax effect of compliance outlays is to reduce tax liability. As a resu lt, in the free cash flow calculation, the tax adjustment generally increases cash flow and business value and, all else equal, reduces the likelihood that a facility will fail the post-compliance closure test. However, the exte nt to which a facility would realize this contribution to cash flow depends on its tax circumstances. In particular, some businesses may not be paying sufficient taxes in the baseline to take full benefit of the implied tax reduction at the facility level – unless the unused tax loss can be transferred to other, profitable busin ess units in the firm, these businesses would not be able to use fully the implied tax reduction on a current basi s. Also, the marginal tax rate for businesses with relatively lower pre-tax income may be less than the co mbined Federal/State tax rate used in the analysis.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

5 For the analysis of options presented in this chapter, none of thes e impact measures (e.g., employment loss, output loss) were in fact relevant because none of the three primary pres entation options resulted in regulatory closures.

B3-10 While businesses may be able to carry forward tax losses to reduce taxes in later years, EPA recognizes that the implied cash flow benefit from tax reduction may not be fully realized, particularly in circumstances involving single-facility firms. To be conservative inits analysis, EPA therefore limited the amount of tax reduction from compliance outlays to be no greater than the amount of tax paid by facilities as reported in the baseline financial statement. The analysis effectivel y assumes that facilities will not be able to offset an implicit negative tax liability against positive tax liabilityelsewhere in the owning firm’s operations or to carry forward (or back) the negative income and its implicit negative tax liability to other positive income/positive tax liability operating periods. On aver age, this approach overstates impacts on facilities, because some businesses may be able to benefit from tax reductions that exceed facility baseline taxes, especially if the facility is owned by a multiple-site firm . Accordingly, EPA constrained the tax effect term in the free cash flow calculation, [- J ( - ) TC - ) D)] as specified above, to be no greater than baseline financial statement tax liability, T.
3. Calculate post-compliance facility value, includi ng post-compliance free cash flow and the compliance capital outlay: As in the baseline analysis, EPA calculated pos t-compliance facility value as the present value of free cash flow and accounting for the compliance cap ital outlay as an undiscounted cash outlay in the first analysis period. Facility post-complian ce business value was calculated as follows:

(B3-6)

where: VALUEPC = estimated post-compliance business value of the facility FCF PC = estimated post-compliance free cash flow CoC = after-tax cost-of-capital (7.0%); t = year index, t = 0-9 (10-year discounting horizon); and CC = compliance capital outlay. EPA considered a facility to be a post-compliance clos ure if its estimated business value was positive in the baseline but became negative after adjus ting for compliance-related cost, revenue and tax effects. In addition to tallying closure impacts in terms of the number of estim ated facility closures, EPA also measured the significance of closures in terms of losses in employment and output. Employment losses equal the number of employees reported by closure facilities in survey responses; output losses equal total revenue reported for regulatory closure facilities. EPA estimated national results by multiplying facility results by facility sample weights. 5 b. Test of Moderate Impacts EPA also conducted an analysis of financial stress short of closure to identify the regulation’s moderate impacts. Facilities incurring moderate impacts are not projected to close due to the proposed Section 316(b) regulation. The regulation, however, might reduce their financial perfo rmance to the point where they incur greater difficulty and higher costs in obtaining financing for future investments. The analysis of moderate impacts examined two financial measures: Pre-Tax Return on Assets (PTRA) : ratio of pre-tax operating income – earnings before interest and taxes (EBIT) – to assets. This ratio measures the operating performance and profitability of a business’ assets independent of financial structure and tax circumst ances. PTRA is a comprehensive measure of a firm's economic and financial performance. If a firm cannot sustain a competitive PTRA on a post-compliance basis, it will likely face difficulty financing its inv estments, including the outlay for compliance equipment.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-11 Interest Coverage Ratio (ICR) : ratio of pre-tax operating cash flow – earnings before interest, taxes, and depreciation (EBITDA) – to interest expense. This ratio measures the facility's ability to service its debt on the basis of current, ongoing financial performance and to borrow for capital investments. Investors and creditors will be concerned about a firm whose operating cash flow does not comfortably exceed its contractual obligations. The greater the ICR, the great er the firm's ability to meet interest payments, and, generally speaking, the greater the firm's credit-carry ing ability. ICR also provides a measure of the amount of cash flow available for equity after interest payments. Creditors and equity investors review the above two measures as criteria to determine whether and under what terms they will finance a business. PTRA and ICR also provi de insight into a firm's ability to generate funds for compliance investments from internally-g enerated equity, i.e., from after-tax cash flow. The measures are defined as follows:
Pre-Tax Return on Assets

(B3-7)

where: PTRA = pre-tax return on assets, EBIT = pre-tax operating income, or earnings before interest and taxes , and TA = total assets. Or, stated in terms of 316(b) income statement accounts, (B3-8)

where: PTRA = pre-tax return on assets; REV = revenue; TC = total operating costs (excluding interest , taxes, and depreciation/amortization); D = depreciation; and TA = total assets. Interest Coverage Ratio

(B3-9)

where: ICR = interest coverage ratio; EBITDA = pre-tax operating cash flow, or earnings before interest, taxes, and depreciation (and amortization) and I = interest expense.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-12 Or, stated in terms of 316(b) income statement accounts,

(B3-10)
where: ICR = interest coverage ratio; REV = revenue; TC = total operating costs (excluding interest, taxes, and depreciation/amortization); and I = interest expenses. Including the effects of 316(b) compliance costs, post-compliance PTRA and ICR are: (B3-11)

(B3-12)

where: PTRApc = pre-tax return on assets, post-compliance; ICR pc = interest coverage ratio, post-compliance; ) TC = change in total facility operating costs (excluding interest, depreciation and taxes), calculated as operating and maintenance costs of compliance; ) D = change in depreciation expense, calculated as compliance capital outlay (CC) divided by 10; CC = compliance capital outlay (assuming all of th e outlay would be capitalized and reported as an addition to assets on the balance sheet); and ) I = incremental interest expense from financing of compliance capital outlay. As a simplifying, conservative assumption, incremental interestexpense is calculated assuming that the compliance capital outlay is fully debt financed at the overall real cost-of-capital of 7.0%. The annual incremental interest value is cal culated as the annualized value of interest payments over 10 years, assuming a constant annual payment of principal and interest. In calculating the baseline values of the PTRA and ICR m easures, EPA applied the same cash flow adjustments as described above for the facility closure analysis, to th e numerators of the PTRA and ICR measures. In the same way as described for the facility closure analysis, these ad justments are intended to capture the change in the financial performance of firms in the Primary Manufacturing Industries between the time of the 316(b) Phase III survey and 2003 (see Appendix 4 to Chapter B3). For evaluating 316(b) manufacturing facilities according to the moderate impact measures, EPA compared baseline and post-compliance PTRA and ICR to 316(b) indus try-specific thresholds that were developed from data compiled by Risk Management Association, Inc. (RMA). RMA compiles and reports financial statement information by industry as provided by member commerc ial lending institutions. The threshold values represent the 25 thpercentile values of PTRA and ICR for statements received by RMA for the eight years from 1994 to 2001 within relevant industries. EPA developed 316(b) industry-level values by weighting and summing the§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

6 See Appendix 6 to Chapter B3 for deta ils of moderate impact thre shold development and secto r-specific threshold values.

B3-13 RMA industry values according to the definition of 316(b) industries 6 . Thresholds by sector ranged from 1.8% to 2.9% for PTRA and from 2.0 to 2.4 for ICR. Because the financial statements received by RMA are for businesses applying for credit from member institutions, the data don’t represent a random sample. In particular, the RMA data likely exclude representation from the fi nancially weakest businesses, which are unlikely to seek financing from RMA member lending institutions. As a r esult, EPA views the threshold values as being relatively conservative and likely to overstate the occurrence of moderate impacts. Both measures are important to financial success and firms’ ability to attract capital. Facilities failing at least one of the moderate impact measures in the baseline were deemed to be already experiencing moderate financial weakness and were not tested for additional financial im pact in the moderate impact analysis. Facilities that passed both moderate impact tests in the baseline but failed one or both threshold comparisons, post-compliance, were considered to incur moderate financial impacts, short of closure, as a result of the proposed Section 316(b) regulation. c. Firm Level Impacts The analysis of impact on firms builds on the facility impact analysis to assess whether firms that own multiple facilities are likely to incur more significant impacts thanindicated by the facility impact analysis. For the assessment of firm-level effects, EPA calculated annualized after-tax compliance costs as a percentage of firm revenue and reports the estimated numbe r and percentage of affected firms incurring compliance costs in 3 costto-revenue ranges: less than 1.0%; at least 1. 0% but less than 3.0%; and 3.0% or greater. EPA’s sample-based facility analysis supports specific es timates of (1) the number of facilities expected to be subject to the regulation and (2) the total compliance cost s expected to be incurred in these facilities. However, the sample-based analysis does not support specific estim ates of the number of firms that own manufacturing facilities. In addition and as a corollary, the sample-b ased analysis does not support specific estimates of the number of regulated facilities that may be owned by a single firm, or of the total of compliance costs across regulated facilities that may be owned by a single firm. For the firm level analysis, EPA therefore considered two cases based on the sample weights developed from the facility survey. These cases provide approximate upper and lower bound estimates on: (1) the number of firms incurring compliance costs and (2) the costs incurred byany firm owning a regulated facility. The cases are as follows: Case 1: Upper bound estimate of number of firms owning facilities that face requirements under the regulation; lower bound estimate of total co mpliance costs that a firm may incur. For this case, EPA assumed (1) that a firm owns only th e regulated sample facility(ies) that it is known to own from the sample analysis and (2) that this pattern of ownership, observed for sampled facilities and their owning firms, extends over the facility population represented by the sample facilities. This case minimizes the possibility of multi-facility ownership by a single firm and thus maximizes the count of affected firms, but also minimizes the potential cost burden to any single firm. For each firm that owns one sample facility, no firm is assumed to own more than one regulated facility, and the analysis is straightforward: the firm owns one regulated facility and incurs compliance costs only for that facility. This configuration is assumed to exist as many as times as the facility's sample weight. However, EPA found that 28% of the firms identified as owning a sample facility, own more than one sample facility. Where the multiple facilities owned by the same firm have the same sample weight , the analysis is also straightforward: the firm is assumed to own and incur the compliance costs of the identified sample facilities, and the configuration is assumed to exist as many times as many times as the uniform sample weight of the multiple facilities.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-14 

### CC CC firm i i 

### = 

# ∑

CC CC firm i i i 

### = × 

# ∑ W In some instances, however, the sample facilities that are owned by the same firm have different sample weights. In these cases, which required a more complex analysis , EPA accounted for the ownership of multiple sample facilities by a single firm, but restricted the count of th e multiple facilities and their configuration of ownership for the firm-level cost analysis b ased on the sample weights of the individual sample facilities. Specifically, the firm is assumed to exist on a sample-weighted basis as many times as the highest of the sample weights among the sample facilities known to be owned by the firm. However, sample facilities with a smaller sample weight, and their compliance costs , can be included in the total instances of ownership by the firm for only as many times as their sample weights. Otherwise, the total facility count implied in the firm analysis would exceed the sample-based estimated total of facilities; correspondingly, the total of compliance costs accounted for in the firm level analysis would exceed the sample-based estimated to tal of facility compliance costs. For implementation, this concept means that all of the sample facilities known to be owned by the same firm, and their compliance costs , can be included in the ownership configuration for only as many sample weighted instances as the smallest sample weight among the multiple facilities owned by the firm. Once the sample weight of the smallest sample weight facility is "used up," a new multiple facility owne rship is configured including only the costs for those facilities with weights greater than the weight of the smallest sample weight facility. This configuration is assumed to exist for as many sample weighted instances as the difference between the lowest sample weight and the next higher sample weight among the facilities owned bythe firm. This process is repeated – with successive removal of the new lowest sample weight facility, and its compliance cost– as many times as necessary until only the highest sample weight facility remains in the ownership configuration. The survey asked respondents to provide firm-level revenue for the parent firm. For single-facility firms, firm revenue and compliance costs are identical to those for the facility. For multi-facility firms, EPA grouped together all facilities with a common parent firm from th e surveys. For each firm in the analysis, firm-level compliance cost is: (B3-13)

where: CC firm = firm-level compliance cost CC i = compliance cost for the surveyed facility i , known to be owned by the firm
Case 2: Lower bound estimate of number of firms owning facilities that face requirements under the regulation; upper bound estimate of total compliance costs that a firm may incur. For this case, EPA inverted the prior assumption and assumed that any firm owning a regulated sample facility(ies), owns the known sample facility(ies) and all of the sample weight associated with the sample facility(ies). This case minimizes the count of affected fi rms, while tending to maximize the potential cost burden to any single firm. For this case, EPA grouped together all facilities with a common parent firm from the surveys and sample weighted the facility compliance costs. EPA calculated the firm-level compliance cost as: (B3-13)
where: CC firm = firm-level compliance cost CC i = compliance cost for surveyed facility iowned by the firm W i = sample weight for surveyed facility iowned by the firm§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-15 As stated above, for the analysis of firm-level impact s, EPA calculated annualized after-tax compliance costs as a percentage of firm revenue. EPA judged that firms with annualized after-tax compliance cost of less than 1.0% of revenue would not be materially affected by the regulation. EPA identified firms as subject to potentially more serious impacts if annualized compliance cost exceeded 3.0% of revenue.



### B3-3 RESULTS This section presents the results of the facility impact analysis. The first section presents the results of the baseline closure analysis. The subsequent sections re port the impact analysis results for the three proposed options. Section B3-3.2 presents the number of facilities with regulatory requirements under the different options. Section B3-3.3 discusses post-compliance closures. S ection B3-3.4 presents the number of facilities with moderate impacts by industry. Section B3-3.5 summari zes total annualized compliance costs on an after-tax basis by option. Section B3-3.6 summarizes the estimated im pacts by option, including facility impacts and total annualized compliance costs on both a pre-tax and after-tax b asis. Section B3-3.7 presents the results of the firmlevel analysis for the two analytic cases described above.
B3-3.1 Baseline Closures Table B3-1 reports estimated baseline closures for f acilities in the Primary Manufacturing Industries and the additional known facilities in Other Industries. EPA dete rmined that 76 facilities (or 14%) of the estimated 532 regulated facilities in the five Primary Manufacturing I ndustries have a negative business value before incurring regulatory compliance costs. The highest percentages of baseline closures occur in the Steel industry sector (43%) and Aluminum industry sector (33%). An add itional four facilities (or 18%) of the 22 known facilities in Other Industries are baseline closures. These facilities are projected to close in the baseline and are not considered in the analysis of impacts attributable to the regulation. Appendix 7 to Chapter B3 provides information on typical establishment closure rates in the 316(b) industries. EPA compared the percentage of facilities assessed as base line closures to typical establishment closure rates in the five Primary Manufacturing Industries, as reported in Statistics of U.S. Businesses (SUSB). SUSB data indicate that between 1.4% and 12.5% of all facilities in these industries, close annually. The percentage of facilities assessed as baseline closures in the Steel and Aluminum industries is higher than the typical closure rates in the industries. However, EPA based its analysis on survey data provided by the facilities and believes these data and analysis provide an accurate representation of the financial condition of the facilities that would subject to the Phase III regulation.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-16 Table B3-1: Summary of Baseline Closures by Sector

Sector Total Number of Facilities Number of Baseline Closures Percentage Closing in Baseline Number Operating in Baseline Paper 230 32 13.9% 198 Chemicals 178 4 2.2% 173 Petroleum 36 5 13.9% 30 Steel 68 29 42.6% 40 Aluminum 21 7 33.3% 14 Total Facilities in Primary Manufacturing Industries 532 76 14.3% 456 Additional known facilities in Other Industries 22 4 18.2% 18 Source: U.S. EPA analysis, 2004.

B3-3.2 Number of Facilities with Regulatory Requirements Of the three options presented here, the 50 MGD Opti on for All Waterbodies, would the largest number of facilities, 133 facilities, or 127 facilities in the Primary Manufacturing Industries and 6 known facilities in Other Industries to national categorical requirements (see Ta ble B3-2). The 200 MGD for All Waterbodies, would subject a smaller set of Manufacturers to national requi rements: 24 facilities, or 22 facilities in the Primary Manufacturing Industries and 2 of the known facilities in Other Industries. The 100 MGD for Certain Waterbodies Option, would subject the smallest number of facilities to national requirements: 20 facilities, or 18 facilities in the Primary Manufacturing Industries and 2 known facilities in the Other Industries.

Table B3-2: Number of Facilities with Regu latory Requirements by Sector and Option

Sector Total Operating in Baseline Number of Facilities with Regulatory Requirements 50 MGD All 200 MGD All 100 MGD CWB Number Percentage Number Percentage Number Percentage Paper 198 37 18.7% 3 1.5% 0 0.0% Chemicals 173 52 30.1% 5 2.9% 7 4.0% Petroleum 30 13 43.3% 3 10.0% 5 16.7% Steel 40 22 55.0% 9 22.5% 6 15.0% Aluminum 14 5 35.7% 1 7.1% 0 0.0% Total Facilities in Primary Manufacturing Industries 456 127 27.9% 22 4.8% 18 3.9% Additional known facilities in Other Industries 18 6 33.3% 2 11.1% 2 100.0% Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-17 B3-3.3 Post-Compliance Impacts Of the 474 facilities potentially subject to regulation af ter baseline closures, EPA estimated that no facilities would close or incur employment losses as a r esult of any of the three proposed options. EPA also found that none of the Manufacturers would incur a moderate impact ( i.e, financial stress short of closure) under any of the three proposed options.
B3-3.4 Compliance Costs Table B3-3 reports the estimated total after-tax comp liance cost to facilities in the Primary Manufacturing Industries and the known facilities in Other Industries by sector and regulatory option. The reported costs exclude costs in baseline closures. The total annualized, after-taxcompliance cost reported in Table B3-3 represents the cost actually incurred by complying firms, assuming no recovery of costs from customers through increased prices and taking into account the reductions in tax liab ility resulting from incurrence of compliance outlays. The after-tax analysis uses a combined Federal/State tax rate, and accounts for facilities’ baseline tax circumstances. Specifically, tax offsets to compliance costs are limited to not exceed facility-level tax payments as reported in facility questionnaire responses. The total annualized, af ter-tax compliance cost reported here is the sum of annualized, after-tax costs by facility at the year of comp liance. This cost calculation differs in concept from the calculation of compliance costs as included in the calculation of the total social costs of the regulation. For the social cost calculation, which is presented in Chapter E1: Summary of Social Costs , the year-by-year stream of total pre-tax compliance costs for all facilities is discounted to the assumed year of promulgation of the 316(b) final regulation for Phase III facilities – i.e., beginning of year 2007 – and then annualized. Two social discount rate values, 3% and 7%, are used in the social cost analysis. Of the three options described here, the 50 MGD Opti on for All Waterbodies, has the highest total after-tax compliance cost, $38.0 million: $32.8 million for facilities in the Primary Manufacturing Industries, and $5.2 million for known facilities in Other Industries. The 100 MGD for Certain Waterbodies has the next higher total after-tax compliance cost, $16.4 million: $15.8 million for facilities in the Primary Manufacturing Industries, and $0.6 million for known facilities in Other Industries. Th e 200 MGD Option for All Waterbodies, would have the lowest cost, $14.5 million: $13.7 million for facilities in the Primary Manufacturing Industries, and $0.7 million for known facilities in Other Industries.

Table B3-3: Total Annualized Facility Compliance Cost aby Sector and Regulatory Option (millions, 2003$)

Sector After-Tax Costs 50 MGD All 200 MGD All 100 MGD CWB Paper $5.0 $2.1 $0.0 Chemicals $15.9 $3.6 $9.0 Petroleum $4.7 $3.0 $3.2 Steel $6.3 $5.0 $3.6 Aluminum $0.8 $0.0 $0.0 Total Facilities in Primary Manufacturing Industries $32.8 $13.7 $15.8 Additional known facilities in Other Industries $5.2 $0.7 $0.6

aThis table reflects the cost incurred by complying businesses and does not represent the cost to society from regulatory compliance. Chapter E1: Summary of Social Costsdiscusses the social cost of the proposed regulation and the other options. The values in th is table exclude baseline closures.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-18 Source: U.S. EPA analysis, 2004.
B3-3.5 Summary of Facility Impacts Table B3-4 summarizes the estimated impacts of the three proposed regulatory options for Manufacturers, as reported in the preceding sections.

Table B3-4: Regulatory Impacts for All Facilities by Option, National Estimates
50 MGD All 200 MGD All 100 MGD CWB Primary Manufacturing Industries Number of Facilities Operating in Baseline 456 456 456 Number of Facilities with Regulatory Requirements 127 22 18 Percentage of Facilities with Re gulatory Requirements 27.9% 4.8% 3.9% Number of Closures (Severe Impacts) 0 0 0 Percentage of Facilities with Regulatory Requirements Predicted to Close 0.0% 0.0% 0.0% Number of Facilities with Moderate Impacts 0 0 0 Percentage of Facilities with Regulatory Requirements with Moderate Impacts 0.0% 0.0% 0.0% Annualized Compliance Costs (aft er tax, million $2003) $32.8 $13.7 $15.8 Additional Known Facilities in Other Industries Number of Facilities Operating in Baseline 18 18 18 Number of Facilities with Regulatory Requirements 6 2 2 Percentage of Facilities with Re gulatory Requirements 33.3% 11.1% 11.1% Number of Closures (Severe Impacts) 0 0 0 Percentage of Facilities with Regulatory Requirements Predicted to Close 0.0% 0.0% 0.0% Number of Facilities with Moderate Impacts 0 0 0 Percentage with Moderate Impacts 0.0% 0.0% 0.0% Annualized Compliance Costs (after tax, million $2003) $5.2 $0.7 $0.6 Source: U.S. EPA analysis, 2004.

B3-3.6 Firm Impacts As previously discussed, EPA’s analysis of firm -level impacts considered two analytic cases:

< Case 1: Approximate upper bound estimate of number of firms owning facilities that face requirements under the regulation; approximate lowe r bound estimate of total compliance costs that a firm may incur, and§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-19 < Case 2: Lower bound estimate of number of firms owning facilities that face requirements under the regulation; approximate upper bound estimate of total compliance costs that a firm may incur. Based on these two analytic cases, EPA estimated the number of firms owning regulated facilities in the Primary Manufacturing Industries to range from 100 (Case 2 estim ate) to 313 (Case 1 estimate), depending on the assumed ownership cases outlined above. An additional 14 firms are known to own facilities in Other Industries. EPA included the additional known facilities in Other Industries in the firm impact analyses but since these facilities have no sample weight (i.e., they are not modeled to represent facilities other than themselves), the upper and lower bound estimates were not applicable to them. Under both Case 1 and Case 2, no firms are estimated to incur total compliance costs equal to or exceeding 1.0% of annual revenue under any of the three proposed options (See Table B3-5, below).

Table B3-5: Firm-Level After-Tax Annual Comp liance Costs as a Percentage of Annual Revenue
Number of Firms in the

Analysis Number and Percentage with After Tax Annual Compliance Costs/Annual Revenue Equal to:

Total No Costs Less than 1% 1-3% At Least 3% Number % Number % Number % Number % Primary Manufacturing Industries a Case 1: Upper bound estimate of number of firms owning facilities that face requirements under the regulation; lower bound esti mate of total compliance costs that a firm may incur 50 MGD All 313 208 66% 105 34% 0 0% 0 0% 200 MGD All 313 292 93% 21 7% 0 0% 0 0% 100 MGD CWB 313 293 94% 21 7% 0 0% 0 0% Case 2: Lower bound estimate of number of firms owning fac ilities that face requirements unde r the regulation; upper bound est imate of total compliance costs that a firm may incur 50 MGD All 100 54 54% 46 46% 0 0% 0 0% 200 MGD All 100 86 86% 14 14% 0 0% 0 0% 100 MGD CWB 100 88 88% 12 12% 0 0% 0 0% Other Industries 50 MGD All 14 10 71% 4 29% 0 0% 0 0% 200 MGD All 14 13 93% 1 7% 0 0% 0 0% 100 MGD CWB 14 13 93% 1 7% 0 0% 0 0%

aTwo known facilities in Other Industries are owned by firms th at own facilities in the Primary Manufacturing Industries and ar e included in this category. Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-20 

### G LOSSARY after-tax cash flow (ATCF): The cash generated from business operations , after-tax, that is available to providers of capital – equity and debt – or for reinvestment in the business. baseline closures:Facilities showing inadequate financial perform ance in the baseline, th at is, in the absence of the regulation. EPA’s analysis assumes these closures would have occurred with or without the regulation. Construction Cost Index (CCI):Measures the cost of a hypothetical package of general construction goods and services compared a base year. The CCI can be used where labor costs are a high proportion of total costs. The CCI uses 200 hours of common labor, multiplied by the 20- city average rate for wages and fringe benefits. (http://www.enr.com/cost/costfaq.asp) cost of capital : Costs incurred for a firm to obtain financing from all capital sources including, in particular, equity and debt. cost pass-through:Calculates the percentage of complian ce costs that EPA expects firms subject to regulation to recover from customers through increased revenue. facility: A contiguous set of buildings or machin ery on a piece of land under common ownership. free cash flow : Cash flow generated by the company that is available to all providers of the company’s capital, both creditors and shareholders. Gross Domestic Product (GDP) Implicit Price Deflator:The GDP Deflator is a quarterly series that measures the implicit change in prices, over time, of the bundle of goods and services comprising gross domestic product. interest coverage ratio (ICR):Ratio of cash operating income to inte rest expenses. This ratio measures the facility's ability to service its debt and borrow for capital investments. liquidation value:Net amount that could be realized by selling the assets of a firm after paying debt. moderate impacts:Adverse changes in a facility's financial position that weaken financial performance and may increase cost of financing but are not threatening to short-term viability. operating and maintenance: Costs estimated to result from operating and maintaining pollution controls adopted to comply with effluent guidelines. Operating costs include the costs of monitoring. pre-tax return on assets (PTRA):Ratio of cash operating income to assets. This ratio measures facility profitability. regulatory closure:A facility that is predicted to close becau se it can not afford the costs of complying with the regulation. severe impacts:Facility closures and the associated losses in jobs, earnings, and output at facilities that close due to the regulation.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-21 

### A BBREVIATIONS ATCF: after-tax cash flow CCI: construction cost index ICR: interest coverage ratio PTRA: pre-tax return on assets§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B3: Impact Analysis for Manufacturers

B3-22 

### REFERENCES Engineering News-Record (ENR). 2004. Construction Cost Index. Available at: http://enr.construction.com/f eatures/conEco/costIndexes/constIndexHist.asp McGraw-Hill and U.S. Department of Commerce, International Trade Administration . U.S. Industry & Trade Outlook

. McKinsey & Company, Inc. et al. 2000 . Valuation: Measuring and Managing the Value of Companies , 3 rd edition. New York: John Wiley & Sons, Inc. Risk Management Association (RMA). 1997-1998. Annual Statement Studies

. Risk Management Association (RMA). 2000-2001. Annual Statement Studies

. U.S. Bureau of Economic Analysis (U.S. BEA). 2004. Gross Domestic Product. Table 1.1.9: Implicit Price Deflators for Gross Domestic Product (GDP) . Last Revised on February 27, 2004. Available at: http://www.bea.doc.gov/bea/dn/nipaweb/TableView.asp#Mid U.S. Department of Commerce (U.S. DOC). 2001. Bureauof the Census. International Trade Administration. U.S. Department of Commerce (U.S. DOC). 1989, 1992, and 1997. Bureau of the Census. Census of Manufactures

. U.S. Department of Commerce (U.S. DOC). 1988- 1991, 1993-1996, and 1998-2001. Bureau of the Census. Annual Survey of Manufactures. U.S. Environmental Protection Agency (U.S. EPA). 2004. Technical Development Document for the Proposed Section 316(b) Rule for Phase III Facilities. EPA-821-R-04-015. November. U.S. Small Business Administration (U.S. SBA). 1989-2001. Statistics of U.S. Businesses

. Available at: http://www.sba.gov/advo/stats/int_data.html Value Line. 1992-2003. Value Line Investment Survey.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Fac ilities Appendices to Chapter B3

B3A-i 

# Appendices to Chapter B3
Appendix 1: Summary of Results for Alternative Options B3A1-1 Number of Facilities with Regulatory Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-1 B3A1-2 Post-Compliance Closures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-2 B3A1-3 Moderate Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-2 B3A1-4 After-Tax Compliance Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-3 B3A1-5 Overview of Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-4 B3A1-6 Firm Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A1-5 Appendix 2: Calculation of Installation Downtime Cost B3A2-1 Estimated Shut-Down Period for Installing Compliance Equipment . . . . . . . . . . . . . . . . . B3A2-1 B3A2-2 Calculating the Impact of Installation Downtime on Complying Facilities . . . . . . . . . . . . B3A2-2 B3A2-3 Calculating the Cost to Society of Installation Downtime . . . . . . . . . . . . . . . . . . . . . . . . . B3A2-4 Appendix 3: Cost Pass-Through Analysis B3A3-1 The Choice of Firm-Specific versus Sector-Specific CPT Coefficients . . . . . . . . . . . . . . . B3A3-1 B3A3-2 Market Structure Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-3 B3A3-2.1 Industry Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-4 B3A3-2.2 Import Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-6 B3A3-2.3 Export Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-7 B3A3-2.4 Long-Term Industry Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-8 B3A3-2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-11 Appendix 4: Adjusting Baseline Facility Cash Flow B3A4-1 Background: Review of Overall Business Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-2 B3A4-2 Framing and Executing the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-4 B3A4-2.1 Identifying the Financial Data Concept to Be Analyzed . . . . . . . . . . . . . . . . . . . . . . . . B3A4-4 B3A4-2.2 Selecting Appropriate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-5 B3A4-2.3 Selecting Industry Groups and Firms for Use in the Analysis . . . . . . . . . . . . . . . . . . . . B3A4-7 B3A4-2.4 Structuring the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-9 B3A4-3 Summary of Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-10 B3A4-4 Developing an Adjustment Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-14 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-18 Appendix 5: Estimating Capital Outlays for Sect ion 316(b) Phase III Manufacturing Sectors Discounted Cash Flow Analyses B3A5-1 Analytic Concepts Underlying Analysis of Capital Outlays . . . . . . . . . . . . . . . . . . . . . . . B3A5-2 B3A5-2 Specifying Variables for the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-4 B3A5-3 Selecting the Regression Analysis Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-7 B3A5-4 Specification of Models to be Tested . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-9 B3A5-5 Model Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-12 Attachment B3A5.A: Bibliography of Literature Reviewed for this Analysis . . . . . . . . . . . . . . . . . . B3A5-17 Attachment B3A5.B: Historical Variables Contained in the Value Line Investme nt Survey Dataset B3A5-18 Appendix 6: Summary of Moderate Impact Threshold Values by Industry B3A6-1 Developing Threshold Values for Pre-Tax Return on Assets . . . . . . . . . . . . . . . . . . . . . . . B3A6-2 B3A6-2 Developing Threshold Values for Interest Coverage Ratio . . . . . . . . . . . . . . . . . . . . . . . . B3A6-2 B3A6-3 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A6-4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A6-5 Appendix 7: Analysis of Baseline Closure Rates§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Fac ilities Appendices to Chapter B3

B3A-ii B3A7-1 Annual Establishment Closures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A7-1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A7-2§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Fac ilities Appendices to Chapter B3

B3A-iii 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-1 

# Appendix 1 to Chapter B3: Summary of Results for Alternative Options

I NTRODUCTION This appendix presents results for 5 additional regulatory options considered by EPA. For these options, facility counts and other results include onlythose Phase III Manufacturers that are (1) nonbaseline closures and (2) subject to national categorical requirements under the option. See the main body of this chapter for a description of data sources and methodologies used in these analyses. In the following tables, the results for these additional opti ons are presented in order of increasing estimated total annualized cost of compliance.



### B3A1-1 N UMBER OF 

### F ACILITIES WITH 

### REGULATORY 

### REQUIREMENTS

Table B3A1.1: Number of Facilities with Regu latory Requirements by Sector and Option

Sector Total Operating in Baseline Number of Facilities with Regulatory Requirements Option 3 Option 4 Option 2 Option 1 Option 6 Paper 198 117 46 117 117 198 Chemicals 173 116 75 116 116 173 Petroleum 30 18 13 18 18 30 Steel 40 33 23 33 33 40 Aluminum 14 8 5 8 8 14 Total Facilities in Primary Manufacturing Industries 456 292 162 292 292 456 Additional known facilities in Other Industries 18 9 7 9 9 18 Source: U.S. EPA analysis, 2004. 

### A PPENDIX 

### C ONTENTS B3A1-1 Number of Facilities with Regulatory Requirements . . . . . . . . . . . . . . . . . . . . . . B3A1-1 B3A1-2 Post-Compliance Closures . . . . . . . . . . . . B3A1-2 B3A1-3 Moderate Impacts . . . . . . . . . . . . . . . . . . . B3A1-2 B3A1-4 After-Tax Compliance Costs . . . . . . . . . . B3A1-3 B3A1-5 Overview of Impacts . . . . . . . . . . . . . . . . B3A1-4 B3A1-6 Firm Impacts . . . . . . . . . . . . . . . . . . . . . . B3A1-5§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-2 

### B3A1-2 P OST 

### -C OMPLIANCE 

### C LOSURES For a description of this analysis, see section B3-2.3 above.

Table B3A1.2: Number of Facilities Es timated as Post-Compliance Closures by Sector and Option

Sector Total Operating in Baseline Number of Post-Compliance Closures Option 3 Option 4 Option 2 Option 1 Option 6 Paper 198 0 0 0 0 0 Chemicals 173 0 0 0 0 0 Petroleum 30 0 0 0 0 0 Steel 40 0 0 0 0 0 Aluminum 14 0 0 0 0 0 Total Facilities in Primary Manufacturing Industries 456 0 0 0 0 0 Additional known facilities in Other Industries 18 0 0 0 0 0 Source: U.S. EPA analysis, 2004.



### B3A1-3 M ODERATE 

### I MPACTS For a description of this analysis, see section B3-2.3 above.

Table B3A1.3: Number of Facilities Estimated as Moderate Impacts by Sector and Option

Sector Total Operating in Baseline Number of Moderate Impacts Option 3 Option 4 Option 2 Option 1 Option 6 Paper 198 0 0 0 0 0 Chemicals 173 0 0 0 0 0 Petroleum 30 0 0 0 0 0 Steel 40 0 0 0 0 0 Aluminum 14 0 0 0 0 0 Total Facilities in Primary Manufacturing Industries 456 0 0 0 0 0 Additional known facilities in Other Industries 18 0 0 0 0 0 Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-3 

### B3A1-4 A FTER

### -T AX 

### C OMPLIANCE 

### C OSTS For a description of this analysis, see section B3-2.3 above.

Table B3A1.4: Total Annualized Facility aAfter-Tax Compliance Cost by Sector and Option (millions, 2003$) Sector Option 3 Option 4 Option 2 Option 1 Option 6 Paper $8.5 $7.2 $9.6 $11.3 $22.9 Chemicals $28.3 $35.1 $35.9 $35.9 $42.0 Petroleum $4.7 $4.7 $4.7 $4.7 $4.8 Steel $7.2 $7.4 $8.1 $8.1 $8.1 Aluminum $0.8 $0.8 $0.8 $0.8 $0.9 Total Facilities in Primary Manufacturing Industries $49.5 $55.3 $59.1 $60.8 $78.8 Additional known facilities in Other Industries $5.6 $5.4 $5.6 $5.6 $5.8

aThis table reflects the cost incurred by complying businesse s and does not represent the cost to society from regulatory comp liance. Chapter E1: Summary of Social Costsdiscusses the social cost of the proposed rule a nd the other options. The estimates in this table exclude baseline and include regulatory closures, and are after-tax. Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-4 

### B3A1-5 O VERVIEW OF 

### I MPACTS For a description of this analysis, see section B3-2.3 above.

Table B3A1.5: Regulatory Im pacts for All Facilities by Option, National Estimates Option 3 Option 4 Option 2 Option 1 Option 6 Primary Manufacturing Industries Number of Facilities Operating in Baseline 456 456 456 456 456 Number of Facilities with Regulatory Requirements 292 162 292 292 456 Percentage of Facilities with Regulatory Requirements 64.0% 35.5% 64.0% 64.0% 100.0% Number of Closures (Severe Impacts) 0 0 0 0 0 Percentage of Facilities with Regulatory Requirements Predicted to Close 0.0% 0.0% 0.0% 0.0% 0.0% Number of Facilities with Moderate Impacts 00000 Percentage of Facilities with Regulatory Requirements with Moderate Impacts 0.0% 0.0% 0.0% 0.0% 0.0% Annualized Compliance Costs (after tax, million $2003) $49.5 $55.3 $59.1 $60.8 $78.8

Additional Known Facilities in Other Industries Number of Facilities Operating in Baseline 18 18 18 18 18 Number of Facilities with Regulatory Requirements 9 7 9 9 18 Percentage of Facilities with Regulatory Requirements 50.0% 38.9% 50.0% 50.0% 100.0% Number of Closures (Severe Impacts) 0 0 0 0 0 Percentage of Facilities with Regulatory Requirements Predicted to Close 0.0% 0.0% 0.0% 0.0% 0.0% Number of Facilities with Moderate Impacts 00000 Percentage of Facilities with Regulatory Requirements with Moderate Impacts 0.0% 0.0% 0.0% 0.0% 0.0% Annualized Compliance Costs (after tax, million $2003) $5.6 $5.4 $5.6 $5.6 $5.8 Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-5 

### B3A1-6 F IRM 

### I MPACTS For a description of this analysis, see section B3-2.3 above.

Table B3A1.6: Firm-level After-Tax Annual Comp liance Costs as a Percentage of Annual Revenue
Number of Firms in the

Analysis Number and Percentage with After Tax Annual Compliance Costs/Annual Revenue Equal to:

Total No Costs Less than 1% 1-3% At Least 3% Number % Number % Number % Number % Primary Manufacturing Industries a Case 1: Upper bound estimate of number of firms owning facilities that face requirements under the regulation; lower bound esti mate of total compliance costs that a firm may incur Option 3 313 114 36% 199 64% 0 0% 0 0% Option 4 313 180 58% 133 42% 0 0% 0 0% Option 2 313 114 36% 199 64% 0 0% 0 0% Option 1 313 114 36% 199 64% 0 0% 0 0% Option 6 313 0 0% 313 100% 0 0% 0 0% Case 2: Lower bound estimate of number of firms owning facilities that face require ments under the regulation Option 3 100 31 31% 69 69% 0 0% 0 0% Option 4 100 48 48% 52 52% 0 0% 0 0% Option 2 100 31 31% 69 69% 0 0% 0 0% Option 1 100 31 31% 69 69% 0 0% 0 0% Option 6 100 0 0% 99 99% 1 1% 0 0% Other Industries Option 3 14 8 57% 6 43% 0 0% 0 0% Option 4 14 9 64% 5 36% 0 0% 0 0% Option 2 14 8 57% 6 43% 0 0% 0 0% Option 1 14 8 57% 6 43% 0 0% 0 0% Option 6 14 0 0% 14 100% 0 0% 0 0%

aTwo known facilities in Other Industries are owned by firms th at own facilities in the Primary Manufacturing Industries and ar e included in this category. Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B3

B3A1-6 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-1 

# Appendix 2 to Chapter B3: Calculation of Installation Downtime Cost

I NTRODUCTION Depending on the engineering design of a facility’s cooling water intake system, installation of some of the compliance technologies considered for the proposed regulation could require a one-time, temporary shut-down of th e facility’s cooling watersystem. During this period, the facility’s coolingwater dependent operations would most likely be halted, with a potential loss of revenue and income from those operations. Accordingly, a key element of th e cost to facilities in complying with the 316(b) Phase III regulation is the loss in income from installation downtime. Installation downtime may also present a cost to society, depending upon assumptions about the cost structure of the production to replace the goods and services not produced by complying facilities during the installation downtime. Unlike the capital and operating cost elements of total co mpliance cost, this cost element is not estimated based solely on engineering analysis of compliance technology sp ecifications. Instead, the cost of installation downtime depends on a number of factors additional to the engineering assessment of compliance requirements. Specifically, the cost of installation downtime depends on the estimated length of time that a facility’s cooling water intake system would be removed from service, the extent to which the facility’s business operations depend on cooling water, and the revenue and operating cost structure of those c ooling water dependent operations. Of these items, the length of time that the facility’s cooli ng water intake system would be out of service was estimated as part of the engineering analysis of complia nce requirements. The remaining items – the extent to which the facility’s business operations depend on cooling water, and the reve nue and operating cost structure of the facility’s cooling water dependent operations – were obtained from the facility’s response to the economic/ financial section of the 316(b) Phase III questionnaire. EPA used this information to calculate the pre-tax income loss from installation downtime. The following sections of this appendix presents the methodology used to estimate the income loss from installation downtime.



### B3A2-1 E STIMATED 

### S HUT 

### -D OWN 

### P ERIOD FOR 

### I NSTALLING 

### C OMPLIANCE 

### E QUIPMENT Installation of some of the compliance technologies cons idered for the proposed regulation would require a onetime, temporary shut-down of the facility’s cooling water intake system. Table B3A2-1, below, lists the estimated durations of net system downtime, in weeks, for eachof the compliance technology modules considered for compliance with the 316(b) Phase III regulation. The net downtime duration accounts for any expected annual period of cooling water system downtime for regular maintenance and repair – the net downtime is the number of weeks the cooling water system would need to be out of service above and beyond any regular maintenance downtime period. Most of the technology modules are expect ed to be able to be installed without any additional net system downtime. However, several of the technology modules are expected to require a net downtime ranging from a month or less to nearly three months. 

### A PPENDIX 

### C ONTENTS B3A2-1 Estimated Shut-Down Period for Installing Compliance Equipment . . . . . . . . . . . . . B3A2-1 B3A2-2 Calculating the Impact of Installation Downtime on Complying Facilities . . . . . . . . . . . . . B3A2-2 B3A2-3 Calculating the Cost to Society of Installation Downtime . . . . . . . . . . . . . . . . . . . . . . . . B3A2-4§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-2 Table B3A2.1: Estimated Average Cooling Wa ter System Downtime by Technology Module Module # Description Net Downtime (Weeks) 1 Fish handling and return system 0 2 Fine mesh traveling screens with fish handling and return 0 3 New larger intake structure with fine mesh, handling and return 2 - 4 4 Passive fine mesh screens with 1.75 mm mesh size at shoreline 9 - 11 5 Fish barrier net 0 6 Gunderboom 0
7 Relocate intake to submerge d offshore with passive fine mesh screen with 1.75 mm mesh size 9 - 11 8 Velocity cap at inlet of offshore submerged 0 9 Passive fine mesh screen with 1.75 mm mesh size at inlet of offshore submerged 0 10 Shoreline tech for submerged offshore 0 11 Double-entry, single-exit with fine mesh and fish handling and return 0 12 Passive fine mesh screens with 0.75 mm mesh size at shoreline 9 - 11 13 Relocate intake to submerge d offshore with passive fine mesh screen with 0.75 mm mesh size 0
14 Passive fine mesh screen at inlet of o ffshore submerged with 0.75 mm mesh size 9 - 11 Source: U.S. EPA analysis, 2004.



### B3A2-2 C ALCULATING THE 

### I MPACT OF 

### I NSTALLATION 

### D OWNTIME ON 

### C OMPLYING 

### F ACILITIES Installation downtime may affect a facility’s business operations in several ways: 1. The facility will be unable to perform production or other business operations that depend on cooling water. 2. The facility will lose revenue from the production and sale of the goods and services that otherwise would have been produced by the affected produc tion operations during the period of downtime. 3. The facility will shed the variable cost of produci ng the goods and services not able to be produced during the period of installation downtime. However, the facility will continue to incur the fixed costs of production associated with the affected operations. 4. If, as part of its cooling water dependent operations, the facility generat es electricity for its own use, and some part of this self-generated electricity conti nues to be needed during the period of installation downtime, the facility may need to purchase replacement electricity. Together, these effects lead to a loss in pre-tax income, which EPA calculated and used as the cost of installation downtime in its analysis of facility impacts. EPA calculated the loss in pre-tax income by first calculating the annual loss in revenue in cooli ng water dependent operations less the variable production costs associated with those operations plus the cost of purchasing electricity to replace any own-generated electricity that is used by the facility. Second, EPA adjusted this annual pre-tax loss value to reflect the length of net installation downtime as estimated in the engineering analysis of compliance technology requirements. Specific elements of these calculations are summarized below for: (1) business effect s not associated with electric power generation and (2) electric power generation-related effects.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-3 Business Effects Not Associated with Electric Power Generation The 316(b) Phase III questionnaire included a series of qu estions aimed at understanding the potential financial effect of temporary or permanent shutdown of a facility’s cooling water intake system. A key data item obtained from the questionnaire response is the fraction of a facility ’s non-electric revenue that depends on cooling water. This information coupled with facility income statem ent information obtained from the questionnaire response provided the basis for calculating the income loss innon-electric power-related operations. Steps in the calculation are as follows: 1. Calculate the annual revenue loss from curta ilment of cooling water-dependent operations by multiplying the fraction of cooling water-dependent revenue times total reported non-electric revenue. 2. Calculate the variable production cost offset to this revenue loss by multiplying materials expense , as reported on the facility’s income statement provided in the questionnaire, times the fraction of cooling water-dependent revenue, as described above. This approach assumes that the variable production cost structure for cooling water-dependent operations is the same as that for non-cooling water-dependent operations. The use of materials expenseas the only component of facility operating costs that may be shed during a period of installation downtime, is re latively conservative as other cost accounts might also be able to be curtailed or the services provided by those accounts – e.g., labor – applied to some other beneficial service within the enterprise. 3. Calculate annual loss in pre-tax income from curtailment of the facilities cooling water intake system from non-electric power-related operations as estimated revenue loss less estimated reduction in variable production cost. 4. Calculate pre-tax income loss in non-electric power-related operations, fro m installation downtime , by multiplying the annual pre-tax income loss by the fraction of the year indicated as the net downtime required for installing compliance equipment.
Business Effects Associated w ith Electric Power Generation The analysis of installation downtime costs for cooling water-dependent electric power generation activities is the same in concept as that outlined for non-electric power-re lated operations, with the exception that facilities may need to incur an additional cost for purchasing replacement electricity if some of the facility’s electric power needs were met from its own generation. Key inform ation obtained from facility questionnaires for calculating the income loss in electric power-related operations in cludes: (1) annual electric revenue reported as cooling water dependent, (2) the fuel cost of electric power gene ration, which is assumed to be shed during the period of curtailed operations, (3) the quantity of electricity consum ed by the facility, and (4) the quantity of electricity generated by the facility. The remaining key input requi red for this analysis is the unit price of replacement electricity: for this item, EPA used the average electricityprice for industrial customers by state, using data from the Department of Energy, Energy Information Admi nistration, for 2002/2003. EPA calculated the pre-tax income loss effect for electric power generation activities as follows. 1. Annual electric revenue from coo ling water-dependent generationis obtained directly from the facility questionnaire. This value is assumed to be the annual revenue loss in electric power generation, from curtailment of cooling water-dependent operations. 2. Annual fuel cost of electric power generationis obtained directly from the facility questionnaire. This value is assumed to be shed during the period of curtailed operations. 3. Calculate own-generated electricitythat is consumed by the facilityas the lesser of (a) the facility’s own electricity generation or (b) the electricity used within the facility.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-4 4. Calculate the quantity of replacement electricity to be purchased by the facility , by multiplying the quantity of own-generated electricity that is consumed by the facility times the fraction of non-electric revenue that is coo ling water dependent but subject to a maximum reduction in electricity need of 75 percent. That is, the facility is assumed to need replacement electricity in proportion to the fraction of non-electric revenue that is not cooling water-dependent. As the fraction of revenue dependent on cooling water, and thus affected by installation downtime, increases, the need for replacement electricity decreases. However, even in the case where the fraction of revenue th at is cooling water dependent is very large (e.g., 100 percent), the analysis assumes that the facility will not shed all of its electricity need: the facility is assumed to always require 25 percent of its baseline electricity consumption from owngenerated electricity. The assignment of the 25 percent minimum electricity replacement need is somewhat arbitrary but reflects the reality that less electricity is likely to be needed to serve a lower level of operations during a cooling water system shutdown, while also acknowledging that all electricity need cannot be shed, regardless of the reduction innon-electric generating activity. The numerical consequence on imposing the 25 percent electricity requirement floor (as opposed to a floor of zero percent) is very small. 5. Calculate the cost of electricity purchased to replace own-generated electricity used by the facility by multiplying the quantity of replacement electricity times the average electricity price, by state, for industrial customers. 6. Calculate annual loss in pre-tax income for electric power-related operations as estimated revenue loss from cooling water-dependent generation less estimated annual fuel cost of electric power generation plus cost of electricity purchased to replace own-generated electricity. 7. Calculate pre-tax income loss in electric pow er-related operations, fro m installation downtime , by multiplying the annual pre-tax income loss by the fraction of the year indicated as the net downtime required for installing compliance equipment. These values are summed to yield the total pre-tax in come loss to the facility from installation downtime. Under the 50 MGD All Option, 19 manufacturing facilities have non-zero downtime. Of these, 18 have non-zero cost of downtime. The facility with no downtime cost s reported that none of its revenue was cooling water dependent. Of the 18 facilities with non-zero downtime cost, downtime cost as a fraction of annual revenue ranges from 0.3% to 14.6%. Under the 200 MGD A ll Option, 3 manufacturing facilities have non-zero downtime. Of these, all 3 have non-zero cost of downtime. Of the 3 facilities with non-zero downtime cost, downtime cost as a fraction of annual revenue ranges fro m 0.3% to 3.0%. Under the 100 MGD SWB Option, 9 manufacturing facilities have non-zero downtime. Of these, all 9 have non-zero cost of downtime. Of the 9 facilities with non-zero downtime cost, downtime cost as a fraction of annual revenue ranges from 0.3% to 3.0%.



### B3A2-3 C ALCULATING THE 

### C OST TO 

### S OCIETY OF 

### I NSTALLATION 

### D OWNTIME The preceding discussion describes the calculation of the pre-tax income loss from installation downtime as used in the facility impact analysis. For the analysis of cost to society, the concept of cost of installation downtime differs from that for the private impact analysis. Specifically, under the assumption that the total quantity of goods and services produced and sold by the affected industries would not change as a result of the regulation (see Chapter E1 for further detail on the social cost analysis framework), the cost to society from installation downtime is the increasein cost for producing the goods and services that would otherwise have been produced by the affected facilities. That is, other producers are assumed to replace the production of goods and services lost due to installation downtime, and the cost to society is the amount, if any, by which the cost of these goods and services exceeds the cost at which the affected facilities would have produced these goods and services. In concept, the cost to society could vary over a br oad range depending on the structure of, and character of competition in, the production of goods and services in the individual markets affected by the 316(b) Phase III regulation.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-5 < At the low end of this possible range, if the replacement goods and services can be provided by other producers at the same variable production cost as otherwise would have been incurred by the affected 316(b) Phase III facilities, then the co st to society of installation downtime would be zero. Because the cost for alternative produ cers is the same as for the producers incurring downtime, society incurs no incremental res ource cost when other producers provide the replacement goods and services. In this case, although the affected 316(b) Phase III facilities incur a financial impact from installation downtim e, this impact – the loss in pre-tax income described in the preceding section – becomes a tr ansfer from the producers incurring installation downtime losses to the producers who make up the lost production.

< At the high end of this possible range, the cost to society would be approximately equal to the pre-tax income loss incurred by facilities due to inst allation downtime. That is, the cost to society would again be the lost revenue from installation downtime less the variable cost of producing the goods and services not produced due to the installation downtime. In this case, the variable production cost for other producers to replace the lost goods and services is assumed to be essentially the same as the pricereceived for the sale of the goods and services not produced by the facilities incurring the installation downtime. This assumption is consistent with a competitive market model of increasing margin al production cost, such that the variable production cost of the marginal supplier of goods and services produced and sold in any period is approximately equal to the price received for those goods and services in the market. The likely reality is that the cost to society fro m installation downtime lies somewhere between these cases. Lacking specific knowledge of the overall production cost structure of the affected industries and for the numerous goods and services provided by the affected industries, to be conservative in its analysis , EPA adopted the latter of the two analytic cases outlined above for its an alysis. That is, EPA assumed that the cost to society from installation downtime would be the same as that estim ated as the pre-tax cost of installation downtime for Manufacturers facilities. To the extent that the variab le production cost for replacement goods and services is less than the selling price of those goods and services, this assumption overstates the cost to society of installation.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 2 to Chapter B3

B3A2-6 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-1 

# Appendix 3 to Chapter B3: Cost Pass-Through Analysis

I NTRODUCTIONThis appendix presents the assessment of cost passthrough (CPT) potential for five key manufacturing industries in which a substantial number of facilities are expected to be subject to the Section 316(b) Phase III regulation. The five industry sectors considered in this analysis are:

< SIC 26: Paper and allied products

< SIC 28: Chemicals and allied products

< SIC 29: Petroleum and coal products

< SIC 331: Steel

< SIC 333/5: Aluminum The purpose of the CPT analysis is to estimate the extent to which cost increases incurred by facilities in complying with the proposed Section 316(b) Phase III regul ation can be reasonably expected to be passed on to consumers in the form of higher prices. This appendix begins with a review of approaches for assessing CPT potential associated with market-wide cost increase scenarios. Next, a description of the methodologyand specific metrics used to assess CPT potential are discussed and the results for each sector provided. Finally, conclusions are presented. From this analysis, EPA concluded that an assumption of zero cost pass-through is appropriate for analyzing the impact of the Phase III regulation on facilities in the manufacturing industries. Performance of the financial impact analysis under this assumption means that facilities must absorb all compliance-related costs and operating effects (e.g., income loss from facility shutdown during e quipment installation) within their baseline cash flow and financial condition. To the extent that facilities w ould be able to pass on some of the compliance costs to customers through price increases, the analysis overstates the potential impact on complying facilities. Thus, this assumption is conservative in avoiding potential overstateme nt of the manufacturing industries’ ability to absorb the costs of 316(b) regulation compliance without material adverse economic/financial impact.



### B3A3-1 T HE 

### C HOICE OF 

### F IRM 

### -S PECIFIC VERSUS 

### S ECTOR

### -S PECIFIC 

### CPT C OEFFICIENTS One method of examining the ability of a firm to p ass-through compliance-related cost increases associated with the Phase III regulation is to review the firm’s histor ical performance in passing on previous cost increases to consumers. For example, Ashenfelter et al. (1998) estimate the cost pass-through rate facing an individual firm, and distinguish that rate from the rate at which a fi rm passes through cost changes common to all firms in an industry, by regressing the price a firm charges on both its costs and the costs of another firm in the industry. The estimated firm-specific CPT rate relates a change in th e prices charged by a specific firm to a change in its production costs, assuming no changes in the production co st for rival producers of that product. However, estimating firm specific CPT rates is extremely complex. For example, in order to estimate firm-specific CPT rates for every Phase III manufacturing firm included in the sample of Detailed Industry Questionnaire (DQ) respondents, EPA would require, for each firm, detailed in formation on the products sold, the markets in which these products are sold, as well as information identifyi ng major competitors in each market. The Detailed Industry Questionnaire did not obtain this information from surveyed facilities. And even if such information were available, the analysis would remain highly challengi ng and subject to significant analytic error. As such, it 

### A PPENDIX 

### C ONTENTS B3A3-1 The Choice of Firm-Specific versus Sector-Specific CPT Coefficients . . . . . . . . . . . . . . . . . . . B3A3-1 B3A3-2 Market Structure Analysis . . . . . . . . . . . . B3A3-3 B3A3-2.1 Industry Concentration . . . . . . . . . B3A3-4 B3A3-2.2 Import Competition . . . . . . . . . . . . B3A3-6 B3A3-2.3 Export Competition . . . . . . . . . . . . B3A3-7 B3A3-2.4 Long-Term Industry Growth . . . . . B3A3-8 B3A3-2.5 Conclusions . . . . . . . . . . . . . . . . . . B3A3-9 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A3-11§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-2 is neither possible nor practical to develop firm-s pecific CPT coefficients for the sample of Phase III manufacturers. Moreover, even if the Agency possessed the data necessary to estimate firm-specific CPT rates, it is questionable whether these rates would be the appropriate measure of CPT potential for compliance-related cost increases stemming from the Phase III regulation. The Phase III regul ation would force multiple firms in each of the industry sectors considered in this analysis to incur co mpliance-related cost increases, which implies that for most firms the cost increases would not only apply to them, but al so to several of their competitors. Not surprisingly, previous studies have found that the CPT rate for changes to an individual firm’s cost differs from the rate at which a firm would pass through cost changes that are co mmon to all, or a substantial fraction of, firms in an industry (Ashenfelter et al., 1998). It can be reasonably e xpected that the higher the share of firms incurring the cost increase, or more appropriately the higher the share of total output produced by such firms, the greater the ability of those firms to pass on a greater portion of those costs to the consumer. In cases where an industry-wide cost shock occurs, an industry-wide CPT rate woul d be an appropriate and practical way of assessing the potential of all firms in that industry to pass through that cost increase to consumers (EPA, 2003). An industry-wide CPT rate provides an estim ate of the change in each facility’s output prices as a function of the increase in its production costs, assuming that the same cost increase is experienced by all firms in the industry. Such an industry-wide rate is relatively easier to estimate than firm-specific cost pass-through rates if one assumes that perfect competition exists in the i ndustry. Among other things, perfect competition implies the existence of product homogeneity within the indus try, homogeneity of production technology among firms in the industry, and homogeneity of production costs among firm s (i.e., pricing is at marginal cost). Under these conditions, the price response to a general industry-wide change in production costs is likely to be industry-wide and similar across all firms. For example, in support of the recently promulgated Metal Products &Machinery (MP&M) industry effluent guidelines, EPA’s Office of Water (OW) estimated industry-specific CPT rates since a large fraction of establishments in these industries were e xpected to be subject to the regulation. EPA estimated these CPT rates by regressing annual output price indices on annual input cost indices for the MP&M industry. The estimated CPT coefficients were validated by a market structure analysis which assessed, for each industry, the potential market power enjoyed by firms in the indus try and the consequent implications it had on their ability to pass through compliance-related costs. Industry-wide CPT rates can be estimated for the Phase III manufacturing sectors based on the methodology used for deriving industry-wide CPT rates for industries cove red by the MP&M regulation. However, because the proposed regulation would only affect those facilities th at operate a CWIS to withdraw cooling water from surface waterbodies, only a subset of facilities in eachindustry sector would incur compliance-related cost increases. As the cost increase associat ed with the proposed regulation is not industry-wide, it is questionable whether industry-wide CPT rates are appropriate for estim ating the price response of firms in the five industry sectors considered in the analysis of Phase III impacts. If a substantial portion of production in each industry occurs at facilities not subject to the proposed regulation, then the use of industry-wide CPT rates may grossly overestimate the ability of firms in these industries to pass-through compliance-related costs to consumers. To assess the reasonableness of using industry-wide C PT rates in the analysis of impacts to Phase III manufacturers, EPA estimated the percentage of total production in each of the five industry sectors considered in this analysis that occurs at facilities potentially subject to compliance-related cost increases. Value of shipments, a measure of the dollar value of production, was selectedfor the basis of this estimate. Because value of shipments data were not collected using the DQ, th ese data were not available for the sample of Phase III manufacturing facilities potentially subject to the proposed regulation. As such, tota l revenue, as reported on the DQ, was used as a close approximation to value of shipme nts for these facilities. EPA estimated the total revenue subject to the proposed regulation by multiplying the 1998 revenue of facilities in the sample of Phase III manufacturers that were determined to be potentially s ubject to the proposed regulation by their facility sample weights and summing across all facilities. Total value of shipments estimates for each industry were obtained from the 1998 Annual Survey of Manufacturers. Table B3A3.1 summarizes the findings of this analysis.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-3 Table B3A3.1: Proportion of Value of Shipmen ts Potentially Subject to Compliance-Related Costs Associated with the Phase III Regulation (1998)

SIC Industry Sector Revenue for Facilities Subject to Phase III Regulation (Millions 1998$) Total Value of Shipments (Millions 1998$) Proportion of Total Value of Shipments Potentially Subject to Phase III Regulation 26 Paper and allied products $55,143 $84,911 65% 28 Chemicals and allied products $61,179 $268,000 23% 29 Petroleum and coal products $47,832 $118,156 40% 331 Steel $38,423 $76,182 50% 333/35 Aluminum $12,096 $19,266 63% Notes: For the purpose of this analysis, fac ility revenue was used as an appropriate surrogate in the absence of value of shipm ents for sample facilities. Source: Section 316(b) Detailed Industry Ques tionnaire and 1998 Annual Survey of Manufacturers.
As shown in Table B3A3.1, the proportion of total va lue of shipments potentially subject to the Phase III regulation ranges from 23 percent to 65 percent dependi ng on the industry considered. The actual proportion of total value of shipments subject to regulation-induced compliance costs would be smaller since not all of the potentially regulated facilities would be subject to meet the national categorical requirements of the proposed Phase III regulation: that is, facilities below the proposed d esign intake flow (DIF) would be subject to permitting based on best professional judgement (BPJ) rather thanbased on national standards, and several facilities currently employ baseline technologies that meet the requi rements of the proposed regulation. Given that less than 65 percent of the total value of shipments in each of the five industries considered in this analysis would be subject to regulation induced compliance costs, and the likelihood that these percentages represent upper bound estimates, EPA believes that the theoretical threshold fo r justifying the use of industry-wide CPT rates in the Phase III impact analysis has not been met. The Agency believes that using industry-wide CPT rates in theanalysis of Phase III impacts would overestimate the cost pass-through ability of firms incurring regulationinduced compliance costs, and thus underestimate impact s. At the other end of the spectrum, however, an assumption of zero CPT would provide a conservative estimate as it would assume that all facilities incur one hundred percent of cost impacts. Given the inability to estimate firm-specific CPT rates and the finding that the use of industry-wide CPT rates would not be appropriate, EPA next conducted a market structure analysis to investigate the extent to which firms in the five industry sectors enjoy sufficient market power to pass compliance-related costs on to consumers in the form of higher prices.



### B3A3-2 M ARKET 

### S TRUCTURE 

### A NALYSIS Information on the competitive structure and market charact eristics of an industry provide insight into the likely ranges of supply and demand elasticities and the sensitivity of output prices to input costs. For example, when input costs increase, the profit-maximizing firm attempts to maintain its profits by increasing output prices, to the extent permitted by market power. The amount of the cost increase that the firm can pass on as higher prices depends on the relative market power of the firm and its customers. The market stru cture analysis described in this section attempts to measure the market power enjoyed byfirms in each of the five industries. This analysis is combined with information from industry review documents such as McGraw Hill’s U.S. Industry and Trade Outlook to reach conclusions regarding the CPT ability of firms in each industry.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

1 The Herfindahl-Hirschman Index was chosen because it provides a more complete pict ure of industry concentration compared to other measures such as the fou r-firm and eight-firm concentration ratios. The HHI uses the market shares of all the firms in the industry, and these market shares are squared in the calculation to place mo re weight on the larger firms. In contrast, the four- and ei ght-firm concentration ratios do not use the market share of all firms in the industry, and nor do they provide information about the di stribution of firm size. For example, if there were a significant change in the market shares among the firms included in the ratio, the value of the concentration ratio would not change.

B3A3-4 The market structure analysis consists of a review of economic data for the following four indicators of market power: industry concentration; import competition; export competition; and long term growth. Each of these indicators is discussed in detail below. EPA notes that the impact of each of these four indicators of market power varies from industry to industry. Furthermore, the results presented for each indicator must be interpreted with caution because even though for a particular indus try an indicator may predict high cost pass-through potential, the specific features of the industry may r esult in the indicator having diminished significance in predicting market power. B3A3-2.1 Industry Concentration The extent of concentration among a group of market participants is an important determinant of that group's market power. A group of many small firms typically has less market power than a group of a few large firms, because the latter are in a more advantageous position to co llude with each other. All else being equal, highlyconcentrated industries are therefore expected to p ass-through a higher proportion of the compliance costs that would result from the Phase III regulation. This analysis uses the Herfindahl-Hirschman Inde x (HHI) as a measure of market concentration 1 . The HHI is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. For example, for a market consisting of four firms with shares of thirty, thirty, twenty and twenty percent, the HHI is 2600 (30 2 + 30 2 + 20 2 + 20 2 = 2600). The HHI takes into account the relative size and distribution of the firms in a market and approaches zero when a market consists of a large number of firms of relatively equal size. The HHI increases both as the numbe r of firms in the market decreases and as the disparity in size between those firms increases. Based on the U. S. Department of Justice’s guidelines for evaluating mergers, markets in which the HHI is under 1000 are c onsidered unconcentrated, markets in which the HHI is between 1000 and 1800 are considered to be moderately concentrated, and those in which the HHI is in excess of 1800 are considered to be concentrated. The accuracy of any analysis of market power originating from industry concentration depends to a large extent on properly defining the relevant market. A well-defined ma rket requires the inclusion of all competitors and the exclusion of all non-competitors. Defining the releva nt market too narrowly overstates market power, while defining the market too broadly would underestimate it. The four-digit SIC category, while not a perfect delineation, is most often used by industrial orga nization economists in their studies because, among publicly available data sources, these industries appear to co rrespond most closely to economic markets (Waldman & Jensen, 1997). Therefore, in Table B3A3.2 below, industry concentration data is presented for each of the fourdigit SIC codes that include at least one potentially regul ated Phase III facility for which DQ data are available.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-5 Table B3A3.2: Herfindahl-Hirsch man Index for Four-Digit SIC SIC SIC Description Industry HHI Unconcentrated Markets (HHI < 1,000) 2679 Converted Paper Products, N. E.C. Paper and Allied Products 143 2899 Chemical Preparations, N.E.C. Chemicals and Allied Products 190 3317 Steel Pipe and Tubes Steel 194 3315 Steel Wire and Related Products Steel 201 2821 Plastics Materials and Resins Chemicals and Allied Products 284 2869 Industrial Organic Chemicals, N. E.C. Chemicals and Allied Products 336 2834 Pharmaceutical Preparations Chemicals and Allied Products 341 2621 Paper Mills Paper and Allied Products 392 2911 Petroleum Refining Petr oleum and Coal Products 414 2865 Cyclic Crudes and Intermediates Chemicals and Allied Products 428 2631 Paperboard Mills Paper and Allied Products 438 3312 Blast Furnaces and Steel Mills Steel 551 3316 Cold Finishing of Steel Shapes Steel 604 2819 Industrial Inorganic Chemicals, N.E.C. Chemicals and Allied Products 677 2873 Nitrogenous Fertilizers Chemicals and Allied Products 792 2611 Pulp Mills Paper and Allied Products 858 Moderately Concentrated Markets (1,000 < HHI < 1,800) 3313 Electrometallurgical Products Steel 1,103 2676 Sanitary Paper Products Paper and Allied Products 1,451 3334 Primary Aluminum Aluminum 1,456 2874 Phosphatic Fertilizers Chemicals and Allied Products 1,528 2841 Soap and Other Detergents Ch emicals and Allied Products 1,584 2813 Industrial Gases Chemicals and Allied Products 1,629 3353 Aluminum Sheet, Plate, and Foil Aluminum 1,633 Concentrated Markets (1,800 < HHI) 2816 Inorganic Pigments Chemicals and Allied Products 1,910 2812 Alkalies and Chlorine Chem icals and Allied Products 1,994 2824 Organic Fibers, Noncellulosi c Chemicals and Allied Products 2,158 2833 Medicinals and Botanicals C hemicals and Allied Products 2,999 Notes: HHI not available for SIC 2823. Source: Economic Census 1992.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-6 Table B3A3.2 reveals that based on their HHI, 16 four-d igit SIC markets can be classified as unconcentrated, 7 can be classified as moderately concentrated, and only 4 can be classified as concentrated. Notably, all 4 of the four-digit SIC categories listed as being concentrated belong to the Chemicals and Allied Products industry. From a market power perspective, Table B3A3.2 seems to suggest that at the four-digit SIC level only four SIC categories are sufficiently concentrated to argue that firms may possess sufficient market power to pass-through a portion of their compliance-related costs assuming that co mpetitor firms in the same industry do not incur similar cost increases. To further examine the level of concentration in each of the five industry sectors, EPA decided to analyze HHI at the industry level as well. The Industry-level HHI for eachsector was calculated as the average of each four-digit SIC HHI belonging to that sector, weighted by the value of shipments for that SIC. EPA notes that aggregating HHI for four-digit SIC categories into industry HHI are lik ely to yield estimates that in general understate market power. Nonetheless, estimated industry HHI should still provi de meaningful insight into market power of firms in the industry because firms in each industry still produ ce similar or related products (for example, paper products, chemicals, and etc.). Estimated Industry HHI are presented below in Table B3A3.3.

Table B3A3.3: Herfindahl-Hirschman Index by Industry SIC Industry HHI 29 Petroleum and Coal Products 414 331 Steel 478 26 Paper and Allied Products 643 28 Chemicals and Allied Products 715 333/5 Aluminum 1,570 Source: U.S. EPA Analysis, 2004.
Table B3A3.3 reveals that, at the industry level, the estim ated HHI for four of the five industries are quite small, implying that they are unconcentrated markets and within these industries individual firms do not enjoy much market power. Notably, the Chemicals and Allied Produc ts industry has a low HHI, which suggests that the 4 four-digit SIC categories that were classified as having c oncentrated markets in reality make up a very small segment of the Chemicals and Allied Products industry. Thus, from the perspective of the Phase III regulation, the majority of firms in this industry have small mark et power. In addition, EPA notes that only 23 percent of production in this is industry would potentially be subject to compliance-related cost increases, which suggests that the cost pass-through potential of firms from this sect or incurring such expenses would be severely curtailed. An important finding in Table 3 is that the Aluminum indus try, which is categorized at the three-digit SIC level, appears to be moderately concentrated. Thus, based so lely on an analysis of industry concentration, it would appear that firms in the Aluminum industry may enjoy moderate amounts of market power, which may enable them to pass through costs at a more than negligible rate. However, as cautioned at the beginning of the market structure analysisok, an accurate judgement of the market power enjoyed by firms in an industry must be reserved until all indicators have been analyzed. B3A3-2.2 Import Competition Theory suggests that imports as a percent of domestic sales are negatively associated with market power because competition from foreign firms limits domestic firms’ ability to exercise such power. Firms belonging to sectors in which imports make up a relatively large proportion of domestic sales would therefore be at a relative disadvantage in their ability to pass-through costs compar ed to firms belonging to sectors with lower levels of import penetration, the measure of import competition used in this analysis. Import penetration, the ratio of§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-7 imports in a sector to the total value of domestic consum ption in that sector, is particularly relevant because foreign producers would not incur costs as a result of the Phase III regulation. In this market structure analysis, EPA assumes that highe r import penetration will generally imply that firms are exposed to greater competition from foreign producers and would thus possess less market power to increase prices in response to regulation-induced increases in produc tion costs. EPA estimated import penetration ratios for each industry as total imports in an industry divided bytotal value of domestic consumption in that industry; where domestic consumption equals dom estic production plus imports minus exports. Import penetration ratios estimated using 1998 census data for the five industry sector s considered in this analysis are presented below in Table B3A3.4.

Table B3A3.4: Import Penetration by Industry
SIC Industry Imports (Millions of 1998$) Implied Domestic Consumption (Millions of 1998$) Import Penetration 26 Paper and Allied Products $13,137 $85,865 15% 28 Chemicals and Allied Products $44,570 $263,404 17% 29 Petroleum and Coal Products $10,711 $120,112 9% 331 Steel $19,221 $88,645 22% 333/5 Aluminum $5,189 $20,063 26% Notes: Implied Domestic Consumption = Value of Shipments + Imports - Exports. Source: 1998 U.S. Bureau of Census data.
The estimated import penetration ratios for the five i ndustries range from 9 percent to 26 percent for the year 1998. The estimated import penetration ratio for the entire U.S. manufacturing sector (SIC 20-39) for the same year is 19 percent. Considering that the United St ates is an open economy, EPA believes it is reasonable to assume that in industries with import penetration ratios close to or above 19 percent domestic firms most likely face stiff competition from foreign firms. Such compe tition is likely to curtail the market power enjoyed by domestic firms and given the scenario that regulation-i nduced cost increases are not incurred by foreign producers would limit the ability of domestic firms to pass-through suchcosts. Thus, based on the import penetration ratios presented in Table 4, only firms in the Petroleum and Co al Products Industry appear to be in a position to passthrough to consumers a significant portion of compliance -related costs associated with the Phase III regulation. However, given the low HHI for this industry EPA believes that existing market competition among domestic firms most likely nullifies any favorable influence the l ack of foreign competitors would have on increasing the market power of firms in this industry. EPA also hi ghlights the above average import penetration ratios for the Steel and Aluminum industries which suggest low market pow er for firms in this industry. With respect to the Aluminum industry, this fact may offset – from a market power perspective – the finding that the industry was identified above as being moderately concentrated. Thus, even though there are relatively few domestic producers in the U.S. Aluminum industry, the notable pr esence of foreign producers in U.S. markets is likely to markedly reduce their the market power. B3A3-2.3 Export Competition The Phase III regulation would not increase the production co sts of foreign producers with whom domestic firms must compete in export markets. As a result, firms in i ndustries that rely to a greater extent on export sales would have less latitude in increasing prices to recover co st increases resulting from regulation-induced increases in production costs. They would therefore have a lower CPT potential, all else being equal.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

2 The period from 1989 to 1998 represents the most recent ten year period that includes data consistent with the survey period for the Detailed Industry Questionnaire (1996-1998).

B3A3-8 This analysis uses export dependence, defined as the percentage of shipments from an industry that is exported, to measure the degree to which a sector is exposed to competitive pressures abroad in export sales. Firms in industries with relatively high export dependence are e xpected to have lesser market power than those in industries with relatively low export dependence due to th eir relatively larger reliance on sales in export markets. Estimated export dependence ratios estimated using 1998 cen sus data for the five industry sectors considered in this analysis are presented below in Table B3A3.5.

Table B3A3.5: Export Dependence by Industry SIC Industry Exports (Millions of 1998$) Value of Shipments (Millions of 1998$) Export Dependence 26 Paper and Allied Products $10,051 $82,778 12% 28 Chemicals and Allied Products $49,932 $268,765 19% 29 Petroleum and Coal Products $5,038 $114,439 4% 331 Steel $5,268 $74,692 7% 333/5 Aluminum $2,951 $17,825 17% Source: 1998 U.S. Bureau of Census data.
The estimated export dependence ratios for the five i ndustries range from 4 percent to 19 percent for the year 1998. The estimated export dependence ratio for the entire U.S. manufacturing sector for the same year is 23 percent. Thus, for all five industries, their export dependence ratio is below the average for the U.S. manufacturing sector. This finding implies that none of the five industries are characterized by strong competitive pressures from foreign firms/markets, and thus market power and CPT potential are not diminished by export dependence. However, it is questionabl e whether this effect works as strongly in the opposite direction, i.e., firms in an industry will have a comparatively high cost pass- through potential simply because firms in that industry are not active in export markets. From the standpoint of fi rms gaining market power, EPA believes that the finding of low export dependence diminishes the importance of export competition as an indicator of market power. Thus, the other three indicators must be relied upon to gauge the amount of market power that firms in each industry are expected to hold. For example, even though the Petroleum and Coal Products and Steel industries have extremely low export dependence, the low market concentration in these industries leads EPA to believe that market power held by individual firms is likely to be quite small. Inaddition, as discussed later in this memo, recent trends in the Steel industry provide good reason to believe that firms in this industry are unlikely to be able to pass through a notable portion of regulation-induced cost increases given the current business environment they face. B3A3-2.4 Long-Term Industry Growth An industry’s competitiveness and the ability of firms to engage in price competition are likely to differ between declining and growing industries. Most studies have f ound that recent growth in re venue is positively related to profitability (Waldman & Jensen, 1997), which sugg ests a greater ability to recover costs fully. To examine trends in long-term growth for each of th e five industry sectors considered in this analysis, EPA estimated the average annual growth rate in the va lue of shipments between 1989 and 1998 for each industry using data available from the U.S. Bureau of Census 2 . EPA expects firms in sectors with higher growth rates to be better positioned to pass through compliance costs rather than being forced to ab sorb such cost increases in order to retain market share and revenue. The resu lts of this analysis are presented in Table B3A3.6.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-9 Table B3A3.6: Average Annual Growth Rates by Industry
SIC Industry Average Annual Growth Rate in Value of Shipments (1989 to 1998) 26 Paper and Allied Products -0.3% 28 Chemicals and Allied Products 1.5% 29 Petroleum and Coal Products 0.6% 331 Steel 0.4% 333/5 Aluminum -2.2% Notes: Average Annual Growth Rate for the Petroleum and Coal Products industry has been estimated for the years 1989 to 1997. The reported value of shipments for this industry in 1998 is significantly lower than in earlier years and therefore was excluded from this analysis as an outlier. Source: U.S. Bureau of Census data.
Table B3A3.6 shows that of the five industries speci fically considered for this analysis, two industries experienced negative growth over the 1989 to 1998 time period and another two experienced only marginal growth. Only the Chemicals and Allied Products i ndustry experienced what may be qualified as moderate growth, displaying an average annual growthrate of 1.5 percent. Based on the U.S. Industry and Trade Outlook ‘99 , annual growth in manufactures over the period 1993 to 1998 was over 2 percent in all years, and over 4 percent in four of the six years in that period (the figures reported for 1997 and 1998 are estimates). An examination of the growth rate for the Chemicals and Allied Products industry for the same six year period (1993- 98) revealed a growth rate of 2.4 percent. Compari ng the average annual growth rate for the Chemicals and Allied Products industry with that of the entire manufact uring sector, and comparing overall growth in this sector with the growth experience by the manufacturing sector taken as a whole between 1993 and 1998 suggests that its performance in general was below average. Thus, inthe absence of strong growth performance during the 1990s for all five industries, it is unlikely that firms in any of these industries acquired significant market power on account of growing demand for their products. In effect, the long-term growth performance of all five industries does not support a conclusion that firms in these industries are in a strong position to pass on a significant portion of their compliance costs.
B3A3-2.5 Conclusions Given that less than 65 percent of the total value of ship ments in each of the five industries considered in this analysis would be subject to regulation-induced comp liance costs, and the likelihood that these percentages represent upper bound estimates, the likelihood that firms in curring such costs would be able to pass through to consumers a material portion of 316(b) compliance costs is small. To validate this hypothesis, EPA undertook the market structure analysis presented in the previous secti on. In general, the weight of evidence from the market structure analysis suggests that firms in all five i ndustries are unlikely to posses significant amounts of market power, thereby lending support to EPA’s hypothesis that mo st firms would not be in a position to pass-through a significant portion of compliance costs. The analysis of individual indicators under the market structure analysis did reveal a few exceptions to the general finding of low market power in all industries. However, considering the combined impact of all four indicators of market power together with information on recent economic trends in these industries suggests that on the whole, firms in each of the five industries hold relatively lo w market power and CPT potential. For example, the estimated HHI for the Aluminum industry indicated that this sector is moderately concentrated, which would potentially allow firms in this industry to pass through a significant portion of their compliance-related costs. In§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-10 contrast, however, the market structure analysis also found that the domestic Aluminum industry witnessed a sustained decline in production during the 1990s and al so faces stiff competition from foreign producers in its U.S. markets. As discussed in the profile of this indus try, in the early 1990s the domestic Aluminum industry was affected by reduced U.S. demand and the dissolution of the Soviet Union, which resulted in dramatic increases in Russian exports of aluminum. The recovery that follo wed was subsequently affected by the economic crises in Asian markets in the second-half of the 1990s, which al ong with growing Russian exports, again resulted in a period of oversupply. These trends, which are reflected in the negative average annual growth rate and high import penetration for the domestic Aluminum industry, suggest that domestic firms in this industry holdrelatively low market power and are not in a position to pass through significant portions of their compliancerelated cost increases. Overall, the balance of the argum ent in favor of and against high cost pass-through in the Aluminum industry rests with the indicators that argue ag ainst it; the lack of domestic competition in the industry is more than offset by the existence of stiff comp etition from foreign producers and the general decline witnessed by the domestic industry. Similarly, in the case of all ot her exceptions found in the market structure analysis, the weight of evidence – when all four indicators of market power are considered together – rests with the indicators that suggest low market power and CPT potential. Based on the findings of the market structure analysis , EPA decided to assume a zero CPT rate for all five industries in the analysis of Phase III impacts. EPA be lieves that this assumption is reasonable given the results of the market structure analysis and is definitely supe rior to using industry-wide CPT rates. In addition, EPA notes that by assuming a CPT rate of zero for all industri es, the analysis of Phase III impacts takes a conservative approach in that the analysis assumes that facilities woul d incur one hundred percent of compliance costs. Thus, whereas an overstated CPT rate may erroneously underes timate impacts for facilities incurring compliance-related cost increases, the use of a conservative CPT rate of zero errs on the side of caution, thus potentially overstating impacts to affected facilities.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-11 

### REFERENCES Ashenfelter, Orley; David Ashmore; Jonathan B. Ba ker; and Signe-Mary McKernan. (1998), "Identifying the Firm-Specific Cost Pass-Through Rate," FTC Working Paper No. 217, January. McGraw-Hill (1999), U.S. Industry and Trade Outlook ‘99. McGraw-Hill Companies. Waldman, Don E. and Elizabeth J. Jensen (1997) , Industrial Organization: Theory and Practice. Addison-Wesley. U.S. Department of Commerce (U.S. DOC). 1989, 1992, and 1997. Bureau of the Census. Census of Manufactures

. U.S. Department of Commerce (U.S. DOC). 1988- 1991, 1993-1996, and 1998. Bureau of the Census. Annual Survey of Manufactures. U.S. EPA, Office of Water (2003), Effluent Limitations Guidelines and New Source Performance Standards for the Metal Products and Machinery Point Source Category.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 3 to Chapter B3

B3A3-12 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-1 

### C HAPTER 

### C ONTENTS B3A4-1 Background: Review of Overall Business Conditions . . . . . . . . . . . . . . . . . . . . . . B3A4-2 B3A4-2 Framing and Executing the Analysis . . B3A4-4 B3A4-2.1 Identifying the Financial Data Concept to Be Analyzed . . . . . . . . . . . . . . . B3A4-4 B3A4-2.2 Selecting Appropriate Data . . . . B3A4-5 B3A4-2.3 Selecting Industry Groups and Firms for Use in the Analysis . . . . . . . . . . B3A4-7 B3A4-2.4 Structuring the Analysis . . . . . . B3A4-9 B3A4-3 Summary of Findings . . . . . . . . . . . . . B3A4-10 B3A4-4 Developing an Adjustment Concept . B3A4-14 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A4-18 

# Appendix 4 to Chapter B3: Adjusting Baseline Facility Cash Flow

I NTRODUCTION To support its analysis of the economic impact of the 316(b) Phase III regulation, EPA collected economic/financial data for the three years 1996-1998 from a sample of facilities in the manufacturing industries primarily expected to be subject to the Phase III regulation. These facility economic/financial data are used to gauge the potential economic/financial impact of regulatory compliance: the facilities and their financial data serve as models for testing the financial effect of regulatory alternatives. For this analysis to provide valid insight into the ability of the affected industries to meet regulatory requirements without material adverse impact, the sample facility data shoul d reflect business conditions that might be reasonably anticipated at the time of compliance. In performing its impact analyses using these data, EPA w as concerned in two ways that the facility survey data might yield erroneous conclusions.

< First, knowing that U.S. business conditions during th e latter half of the 1990s were cyclically strong, EPA was concerned that business conditions duri ng the 316(b) survey period (1996-1998) might be abnormally favorable for some of the five manufacturing sectors covered in the Phase III analysis. In this case, the business performance and valuation measures, which are based on survey data, used to assess the burden of regulatory compliance costs might overstate industry's ability to bear these costs and therefore understate the potential impact of the Phase III regulation.

< Second, apart from the issue of short-term deviation from trend caused by a cyclically strong economy, EPA was also aware from its profile analyses that some of the industries might be experiencing a longer-term trend of deteriorating performance. Using sample facility data that don't reflect such possible trends would again potentially ove rstate industry's ability to bear compliance costs and therefore understate the potential impact of the Phase III regulation. Given these concerns, EPA analyzed for the manufact uring industries (1) whether business conditions were “abnormally favorable” during the survey period and (2 ) whether business performance over a longer term might be following a non-neutral – in particular , negative – trend. This analysis validated EPA’s concerns that use of unadjusted survey data might yield e rroneous conclusions from the facility impact analysis. From the findings of this analysis, EPA developed a basis for adjusting surveyfinancial data to account for these effects: short-term deviation from trend and non-neutral trend. This appendix documents EPA’s anal ysis and development of adjustment factors for the 316(b) Phase III manufacturing industries.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-2 

### B3A4-1 B ACKGROUND 

### : REVIEW OF 

### O VERALL 

### B USINESS 

### C ONDITIONS As background for its analysis, EPA reviewed general ec onomic data over the past several years to assess whether business conditions during the survey data collecti on period of 1996-1998 might be generally perceived as abnormally favorable for the U.S. economy, as a whole. This review confirmed the concern that business conditions in 1996-1998 were generally mo re favorable than the average of conditions over a longer time period. Figures B3A4.1-B3A4.3 present annual and average values for the period 1985-2003 for three measures of general economic performance: Figure B3A4.1: Growth in Real Gross Domestic Product, 1985-2003 . This exhibit, based on data published by the Department of Commerce, Bureau of Economic Analysis, focuses on the growth trend of the broad economy, including all sectors. Growth stronger than the average trend would indicate a strongly expanding economy and would generally indicate strong business performance. Figure B3A4.2: Capacity Utilization in Manufacturing Industries, 1985-2003 . This exhibit, based on U.S. Federal Reserve Bank data, reports th e rate of capital utilization for all manufacturing sectors. All else equal, when the rate of capital utilization is higher than the average trend, demand for manufacturing output is strong and manufacturing business performance would be generally strong. Figure B3A4.3: Growth in Industrial Production, 1985-2003 . Like the preceding exhibit, this exhibit is based on data published by the U.S. Federa l Reserve Bank and reports the rate of growth in the Federal Reserve's Industrial Production Index, which is a measure of the real output of the manufacturing industries. Growth stronger than the average trend would indicate a str ong expansion in the manufacturing industries and would generally indicate strong manufacturing business performance. In each case, the annual values in the period 1996-1998 are above the average trend line, indicating stronger overall economic performance in the survey data collec tion period than for the longer period presented in the charts. The data show a consistent year-by-year pattern over the 1996-1998 period:

< 1996: The values for 1996 are above the longer-term averag e trend but are the lowest of the values for the three years.

< 1997: The values for 1997 are the highest of the values for the three years.

< 1998: The values for 1998 fall between the 1996 and 1997 values. In the case of industrial production and capacity utilization in manufacturing industries , 1997 is the peak performance year over the 1990s decade and is followed by a decline in 1998 and sub sequent years leading to the recession period in 2001. In the case of GDP growth , the fall-off in 1998 (from 1997) is followed by one more year of strong growth in 1999. Afterwards, GDP growth turns sharply lower during 2000, the recession year of 2001, and subsequent years. As is widely acknowle dged in general business conditions analyses, economic weakness during the 2000-2003 period began earlier in th e manufacturing industries than in the general economy.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-3 -1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0%

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

YearYear to Year Growth in GDP, Percent

Grow th, Real GDP Average Grow th, GDP

70.00 72.00 74.00 76.00 78.00 80.00 82.00 84.00

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 YearCapacity Utilization, Percent

Capacity Utilization Average Capacity Ut ilization Figure B3A4.1: Growth in Real Domestic Product, 1985-2003

Figure B3A4.2: Capacity Utilization in Manufacturing Industries, 1985-2003§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-4 -4.0% -2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

YearYear to Year Growth in Industrial Production

Grow th, Industrial Production Average Grow th, Industrial Production Figure B3A4.3: Growth in Industrial Production, 1985-2003



### B3A4-2 F RAMING AND 

### E XECUTING THE 

### A NALYSIS The objective of this analysis was to understand (1) th e extent to which the business conditions and financial performance of the Phase III manufacturing industries reflect ed cyclically favorable conditions during the 316(b) survey period and (2) whether these industries show a non-neutral longer term trend in economic/financial performance – e.g., deterioration in performance over time inde pendent of cyclical variation. If either or both of these conditions were found, then the data used to test for these conditions would be used to adjust relevant survey data items to a level consistent with normal business conditions and/or the longer term of performance. To meet these objectives, EPA set, as its overall approach, identification and analysis of a financial performance data series for the 316(b) manufacturing industries. This data series would be used to test whether financial performance at the time of the 316(b) survey differed fro m the longer term trend. At the outset, EPA recognized that, in all likelihood, such a data series would not re port financial performance at the level of the individual facility – which is the unit of analysis for the 316(b) f acility impact analysis – but would report performance for individual firms or for some industry aggregate. As a r esult, EPA would need to infer the trend of performance in facility financial performance from firm- or industry-level pe rformance and, in turn, apply adjustments, if needed, to facility financial data based on analysis of the firm - or industry-level performance. Although the use of firmor industry-level information for adjusting facility data necessarily represents a limitation in this analysis, EPA judges that the effort is warranted given: (1) the potential for the facility impact analysis to yield erroneous findings if it is based on data that reflect cyclically favorable conditions and (2) the absence of facility data to support a more precise analysis. Key steps in framing and executing th e analysis are described below.
B3A4-2.1 Identifying the Financ ial Data Concept to Be Analyzed EPA determined that the financial data concept to be anal yzed should be equivalent, or close in concept, to the business performance and valuation metrics used in the Phase III impact analysis. For the facility impact analysis, the key financial metric is after-tax, pre-interest cash fl ow, calculated as income before interest, depreciation and amortization, and adjusted to be on an after-tax basis. In the facility impact analysis, this metric is used to§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-5 calculate the business value of a sample facility, on both a baseline – i.e., before imposition of compliance costs – and post-compliance basis. Using this, or a closely related, measure in the analysis of financial performance at the time of the facility survey would therefore support a dir ect test of whether and how the survey financial data – to be subsequently used in the facility impact analysis – might reflect cyclically favorable conditions or differ from the longer term trend of financial performance in ananalysis. If either or both of these conditions were found, the data would also readily support development of a necessary adjustment to offset these potential biases in the survey data. EPA recognized that the after-tax, pre-interest cash flow measure used in the facility impact analysis would very likely not be directly available from financial datasets that might be practically used in this analysis. However, reasonable surrogates for this measure that would likely be available include: after-tax cash flow from operations (net income plus depreciation and amortization); earnings before interest, taxes, depreciation and amortization (EBITDA); net income; and earnings before interest and taxes (EBIT).
B3A4-2.2 Selecting Appropriate Data Other key requirements of the data to be used in the analysis include:

< The financial data need to be a time series, pr eferably annual, over a sufficiently long period (and including the survey period) to allow testing of (1) whether survey period business conditions were cyclically favorable; and (2) whether financial perfo rmance in the industries exhibits a longer-term, nonneutral trend.

< The data need to be at a sufficient level of i ndustry resolution to account for variations in business conditions and performance not only across the five manufacturing sectors but also within certain sectors, where there may be substantial variation in performan ce by important segments. Of particular importance is the ability to segment the chemicals sector into its segments such as basic chemicals and pharmaceuticals, and the primary metals sector into the ferrous and non-ferrous metals segments. Based on these requirements, EPA selected the Value Line Investment Surveyfirm financial dataset as the data source for this analysis. The Value Linedataset meets analysis requirements as follows:

< The general company dataset of the Value Line I nvestment Survey (VL) reports summary financial information for nearly all publicly traded compan ies in the United States for a 12-year period, 1992-2003, which includes the 1996-1998 Phase III survey period. The individual years in this 12-year period may be categorized in three broad categories of economic performance: (1) six years of "normal" economic performance - 1992-1996 and 2000; (2) three years of "subnormal" economic performance - 2001-2003; and (3) three years of years of "supra-normal" economic performance - 1997-1999. The 12-year period thus captures reasonable diversity of business conditions before, after, and during the survey period. By including financial results for full-year 2003, the dataset also comes as close as possible to the present (2004) and thus would provide a basis for adjusting facility baseline financial data to essentially current conditions.

< VL identifies and groups companies in a business cont ent classification scheme that approximates 3-digit SIC or 4-digit NAICS classifications. These business classifications support identification of firms within the Phase III manufacturing industries at a level of sector detail sufficient for this analysis. Because (1) the dataset is by company instead of by aggregat e groups and (2) the business classifications are defined by practical business content instead of in a rigi d SIC or NAICS classification scheme, the VL dataset§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

1 As described in the industry profiles, the change from SI C-based to NAICS-based reporting of economic data by federal government and other data sources at around 1997/98 created difficu lties in aligning and ensuring consistency of time series data that are organized within these frameworks.
B3A4-6 avoids the challenge confronted elsewhere in the Phase III analysis of the change in economic classification schemes and resulting inconsistency of a ggregated data series over the year of the change 1

.

< The VL dataset reports key accounting items that w ill readily support calculation of a financial metric, after-tax cash flow, that very nearly matches the principal financial metric (after-tax, pre-interest cash flow) underlying the Phase III facility impact analysis. EPA recognizes that the VL dataset, by definition, excl udes firms that are not publicly traded. The studied industries include private, non-publicly traded firms, for which no comparable database of financial information is available. As a result, use of the VL dataset in this an alysis could yield findings that are not representative of the overall industry, including the non-publicly traded firms, to the extent that non-public firms in the studied industries faced materially different business conditions or achieved materially different business performance than publicly traded firms in the same industries. Over all, EPA expects that the business conditions faced by, and performance achieved by, non-public firms in the studied industries are not likely to have been materially different from those of the public firms. As a result, EPAjudges that use of the VL dataset for this analysis is appropriate and likely to yield reasonably representativ e findings for to overall industries, including publicly traded and non-traded firms. In addition to the VL dataset, EPA consider ed a range of other data sources, including:

< Economic and business performance data published bythe Federal Reserve, in particular the Federal Reserve Economic Data ( FRED II ) data series compiled by the Federal Reserve Bank of St. Louis.

< The Quarterly Financial Report for Manufactu ring, Mining, and Trade Corporations (QFR) published by the U.S. Census Bureau.

< Data series from the Bureau of Economic Analysis and data specifically available in The Survey of Current Business

. These data sources were each deficient for the an alysis in some material way, including:

< Data being too aggregate to provide the industry sect or and sub-sector level of resolution needed to assess business conditions and trends within the Phase III manufacturing sectors.

< Data items being descriptive of general economic/fin ancial conditions in an industry but not being sufficiently close to the financial perfo rmance concept neededfor the analysis.

< Data being reported in inconsiste nt economic classification frameworks over the desired analysis period. In addition to the problem of the SIC/NAICS break itself, data are sometimes reported at different levels of resolution before and after the SIC/NAICS break – e.g., at a 4-digit or finer level in the NAICS framework but only 2-digit level of resolution in the SIC framework.

< Data not being readily available in an electronic form at needed for efficient pe rformance of the analysis.
B3A4-2.3 Selecting Industry Groups and Firms for Use in the Analysis As discussed above, VL organizes firms by industry groups, which, in most instances, approximate 3-digit SIC or 4-digit NAICS classifications. From review of the VL industry groups and the 316(b) Phase III manufacturing industries, EPA selected 10 VL industry groups and the firms within these industry groups as candidates for this§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

2 EPA considered removing the non-recurring item from the income statement but, because of uncertainty about the correct tax adjustment, rejected this approach.
B3A4-7 analysis. Following review of the firms within these industry groups, EPA retained 6 firms for use in this analysis. Key considerations in selecting the firms are as follows:

< The selected VL industry groups are those thatmost closely correspond to the 316(b) Phase III manufacturing industries.

< Within the industry groups, only those firms whose business operations reasonably match the profile of business activities of the 316(b) Phase III manufacturing industries were considered candidates for the analysis. In some industry groups, a substantial number of firms included in the VL industry groups were excluded from the analysis:

< VL includes Aluminum industry firms in its Metals and Mining industry group. However, most firms in this VL industry group are not involved in the Aluminum industry and thus were excluded from the analysis dataset.

< EPA retained from the Paper and Forest Products group only those firms engaged in pulp mill, paper mill and/or paper and paperboard manufacturing operations. Firms engaged only in timber and lumber production were excluded from the analysis.

< EPA retained only those firms that are based in th e United States or Canada, and for which financial information is available in U.S. dollars.

< After defining an initial set of firms according to th ese procedures and criteria, EPA retained only those firms for which a full 12 years of data were available.

< Finally, EPA excluded firms that had undergone a significant restructuring – e.g., a merger or acquisition – which materially disrupted the continuityof financial reporting. Since the analysis to be performed would start from a time series of cash fl ow, measured in absolute dollars – as opposed, for example, to a time series of profit percentage values – including data from firms whose continuity of financial reporting had been affected by merger or acquisition activity would tend to bias the analysis. In particular, firms engaging in mergers and acquis itions that were accounted for on a purchase-accounting basis instead of a pooling-of-interests basis, woul d be likely to show sudde n jumps in revenue, net income, and cash flow. These sudden jumps would bias the analysis by suggesting greater business growth than could be reasonably be achieved by the fi rm or facilities within the firm on a simple, organic growth basis. Similarly, large contractions in business volume resulting from divestiture or termination of a line of business would bias the analysis in the downward direction. To apply this restriction, EPA examined the year-to-year revenue profiles for all fi rms over the 12-year analysis period. EPA researched annual reports and other financia l reporting for those firms showing large increases or decreases from year to year and excluded those firms where a material business event was found that would otherwise disrupt the continuity of financial reporting. EPA followed this rule with only two exceptions. First, EPA kept firms in the analysis when the only business event/disruption of financial reporting occurred in the last year of financial reporting – 2003. EPA kept these firms in the analysis but excluded the final year of data from the analysis. Second, in its research on one firm in the paper industry, EPA found that the firm had recorded an unusual, non-recurring stock gain trans action in 1995 that caused revenue and net income to increase abnormally in that year. A lthough the VL net income item used in the analysis generally excluded income from unusual, non-recurring events, the VL data series did not exclude income from this transaction. Because EPA had already set aside a substantial number of firms from the paper industry, EPA decided to keep this firm in the analysis but exclude the single year of unusual financial performance from the analysis dataset 2

. Applying this restriction subs tantially reduced the number of§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

3 Because no Aluminum industry firms were able to be retained in the analysis, EPA was unable to develop an after-tax cash flow adjustment factor for facilities in the Aluminum industry. EPA c onsidered adjusting the pre-event financial statements for the Aluminum industry firms – in effect, converting the purchase-accounting treatment of transactions to a pooling basis – but rejected thisapproach as requiring too many judgments. However, EPA assessed the potential effect of applying a cash flow adjustment factor to facilitie s in this industry by testing hypothetical factor va lues that substantially ex ceeded the adjustments – both for decrease and increase – e stimated and applied for the facilities in other industries. This analysis found that the facility im pact analysis results for the Aluminum industry did not change over this wide range of hypot hesized cash flow adjustment factors.
B3A4-8 firms that were included in the analysis dataset. In particular, for the Aluminum industry, all of the firms in the initial VL dataset were found to have some significant discontinuity of financial reporting. 3 EPA organized these 60 firms into six 316(b) Industry Groups for the analysis. Table B3A4.1, below, lists the VL industry groups, the 316(b) Phase III manufacturing industries and/or industry segments (as discussed in the industry profiles) to which the VL industry groups appr oximately correspond, the 316(b) Industry Groups for this analysis, and the number of VL firms used in the analysis for each industry group.

Table B3A4.1: Value Line Industry Groups Selected for Analysis Value Line Industry Group 316(b) Phase III Manufacturing Industry 316(b) Industry Segment(s) (as relevant) 316(b) Phase III Industry Group for Analysis Number of Firms Used in Analysis Metals and Mining Aluminum Aluminum none Paper and Forest Products Pulp and Paper Mills Pulp and Paper Mills 6
Chemical (basic) Chemicals Industry Organic Chemicals Inorganic Chemicals Industrial Chemicals 15 Chemical (diversified) Chemicals Industry Organic Chemicals Inorganic Chemicals Chemical (specialty) Chemicals Indus try Plastics Material and Resins Plastics Material and Resins 15 Biotechnology Chemicals Industry Pharmaceuticals Pharmaceuticals 4 Drug Chemicals Industry Pharmaceuticals Petroleum (integrated) Petroleum Refining Petroleum Refining 6 Steel (General) Steel Steel 14 Steel (Integrated) Steel Source: Value Line Investment Survey and U.S. EPA analysis, 2004.
B3A4-2.4 Structuring the Analysis The general objectives of this analysis were to:

< Test, by 316(b) Industry Group, whether after-tax cash flow performance deviated, during the 316(b) survey data collection years, from normal performance over the 12-year analysis period.

< Test, by 316(b) Industry Group, whether after-tax cash flow performance might be following a nonneutral trend over the 12-year analysis period.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-9 < Given a finding that either or both of these conditions ar e true, to develop an adjustment to baseline aftertax cash flow to account for these effects, and, to yi eld after-tax cash flow values for the facility impact analysis that more closely reflect current financial performance in the 316(b) Phase III industries. The overall approach to the analysis was to analyze, for each industry group, the trend of financial performance over the 12-year analysis period and to assess where the indus try’s financial performance lay relative to that trend during the 316(b) survey data collection years of 1996-1998. For each industry group. EPA used as analysis observations, an index of constant do llar-adjusted after-tax cash flow for each of the firms in the industry group. To analyze the trend, EPA calculated a simple regr ession of the index values against time. The estimated regression relationship provided a direct measure of the real (i.e., inflation-adjusted) trend of financial performance for each industry group. The 1996-1998 averag e of index values for each industry group were then compared with the trend values predicted from the estimated regression coefficients – both for the 1996-1998 years and for the end of the analysis period – to dete rmine the extent to which 1996-1998 survey values should be adjusted to reflect (1) the deviation from trend at 1996-1998 and (2) the trend from 1996-1998 to the end of the analysis period. Specific steps in this analysis were as follows:

< Calculate After-Tax Cash Flow (ATCF) as Net Profit plus Depreciation for each firm by year . As discussed above, EPA sought to analyze ATCF as a close approximation of the key financial metric – after-tax, pre-interest cash flow – used in the fac ility impact analysis. EPA calculated ATCF on a yearby-year basis for each firm in the analysis dataset as the sum of the VL data items: Net Profit and Depreciation. In the VL data framework, Net Prof it is defined as net income from continuing operations and excluding non-recurring items. Depreciation includes both non-cash items, depreciation and amortization.

< Adjust ATCF to constant dollar values at mid-year 2003, using the GDP deflator . To eliminate the effects of inflation in analyzing the trend of financial perfo rmance, EPA deflated the ATCF values for all firms to mid-year 2003 using the GDP Deflator series published by the Department of Commerce, Bureau of Economic Analysis.

< Calculate an index of each company’s ATCF values byyear using, as an index numerator, the average ATCF value for the company over the 12-year period . As summarized above, the overall approach of the analysis involved a regression analysis of the trend of ATCF values for the firms in an industry group over the 12-year analysis period. To allow individua l firms’ ATCF values to be combined in a single regression requires eliminating the scale effect of the different sizes of firms. For this reason, the inflation-adjusted ATCF values for each firm were normalized to an index series by dividing the yearly values by the average of values for each firm over th e 12-year period. EPA used the 12-year average of values for this index calculation instead of the value for a single year to prevent anomalously large swings in the index series when the ATCF value for the y ear selected as the base year for the index calculation was very small relative to other values in the 12-year series. In addition, in calculating the index values for the 12-year series, EPA first removed any negative values from the series for each firm by adding to each value in the firm’s 12-year series, the absolute va lue of the most negative value for the firm plus the absolute value of the smallest non-negative value inthe series. This adjustment has the effect of “vertically” shifting the ATCF values for a firm so that all values are positive while retaining the mathematical “shape” of the series for the trend anal ysis. This adjustment was necessary to prevent the undesirable inversion of the index trend that would o ccur if a negative index numerator is combined with a positive series values in calculating the index series.

< Regress ATCF index values against year by industry gr oup to calculate the time trend of constant dollar ATCF over the period 1992-2003 . The preceding calculations yield a constant dollar series of ATCF indexed to one and with an average value of one over the 12-year analysis period. To calculate the trend indicated by these index ATCF values, EPA estimated a weighted linear regression of the index ATCF values against year by industry group. These regressi ons were performed on a revenue-weighted basis –§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

4 In addition to testing a simple linear m odel of index ATCF against time, EPA also used a logarithm-adjusted series of the ATC F values to test for an exponential trend in ATCF. The log m odel provided no improvement in th e estimated regression relationshi ps. As a result, EPA used the coefficients estimated from th e linear model for its analysis of the ATCF trend.
B3A4-10 i.e., each ATCF value was weighted by the firm’s revenue value for the year – so that each firm’s individual ATCF trend carries a weight in proportion to its revenue in estimating the trend relationship. As a result, the estimated trend relationship reflect s a revenue-weighted average of the ATCF trends of the individual firms, instead of an arithmetic aver age, which would overweight the presence of smaller firms in estimating the trend relationship. The es timated ATCF index coefficient from the regression for each industry indicates the trend in constant dollar AT CF over the analysis period: a negative coefficient indicates declining constant dollar ATCF over the analysis period; a positive coefficient indicates growing constant dollar ATCF over the analysis period. 4

< Calculate the predicted trend of ATCF index values for each industry group . Used together, the estimated ATCF index coefficient and regression intercept yield a predicted trend line of ATCF index values for the 12-year period , for each industry group analyzed. The actual ATCF index values for an industry group can then be compared with predicted trend line to assess whether the ATCF values during the 1996-1998 survey data collection period deviate from the trend. The predicted trend line also indicates wherethe ATCF index values would be at the end of the anal ysis period if the ATCF index values followed the predicted trend.

< Calculate the revenue-weighted average of actual ATCF index values by industry group for the 1996- 1998 period . The revenue-weighted average of actual ATCF values for the 1996-1998 period is compared with the predicted trend values to assess th e extent to which the actual ATCF index values deviate from trend and to provide a basis for estimati ng the adjustment needed to bring the ATCF values to the trend, or to the predicted trend value at the end of the analysis period. These values were calculated by, first, averaging the 1996, 1997, and 1998 ATCF index values for each firm, and, second, averaging these firm-average values over the firms in an industry group using, as weights, the 1996-1998 average revenue of each firm in the industry group. 

### B3A4-3 S UMMARY OF 

### F INDINGS Table B3A4.2, below, summarizes key r esults from the analyses outlined above. Items reported in the table are as follows:

< Estimated Trend : the revenue-weighted average of annual change in ATCF Index values for firms in the industry group over the analysis period, 1992-2003. Th is value is the estimated coefficient of ATCF Index against time from the simple linear regression, as described above. Because the trend is estimated from an index series with an average value of one ove r the analysis period, the estimated trend may also be interpreted as equal approximatelyto the annual percent change in ATCF Index. A negative estimated trend value indicates that ATCF Index values declin e, on average, over the analysis period; a positive estimated trend value indicates that ATCF Index va lues increase, on average, over the analysis period.

< Average of ATCF Index Values at 1996-1998 : the revenue-weighted average of actual ATCF Index values for firms in the industry analysis set over the 1996-1998 period. The values in this column are compared with values in the next two columns, resp ectively, to assess (1) the extent to which financial performance during the 1996-1998 survey data peri od deviated from the analysis period trend at 1996- 1998 and (2) the overall change in financial perform ance from the 1996-1998 survey data period to the end of the analysis period resulting from the combination of deviation from analysis period trend and the trend, itself (see following paragraphs for further discussion).

< Average of Predicted Trend Values at 1996-1998 : the average of predicted ATCF Index values over the 1996-1998 survey data period as predicted from the estimated regression terms . If ATCF performance for an individual firm or industry matched the indus try trend over time, the actual ATCF Index values at§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-11 1996-1998 would equal this value. Material deviation of the actual ATCF Index values from this value suggests, for an industry, that ATCF performance during the 1996-1998 survey data period was: (1) abnormally favorable, if the actual ATCF Index values exceed the average of predicted values, or (2) abnormally unfavorable, if the actual ATCF Index values are less than the average of predicted values.

< Predicted Trend Value at 2003 : the ATCF Index value at the end of the analysis period as predicted from the estimated regression terms . This value is the (statistically) e xpected value of ATCF Index at 2003 for a firm or for the industry, based on the estimated trend relationship. Material deviation of this value from the Average of Predicted Trend Values at 1996-1998 indicates a general trend going forward from the 1996-1998 survey data period, which, apart from cycli cal deviation, which would further affect ATCF performance for firms in the industry group. For an industry, if this value is less than the Average of Predicted Trend Values at 1996-1998, then financial pe rformance, as indicated by ATCF Index, generally deteriorated from 1996-1998 forward to 2003, the end of th e analysis period. In addition to the cyclical deviation effect, this “trend” effect might also be taken into account in adjusting ATCF data calculated from the 316(b) survey responses. In th is case, comparison of the average of actual ATCF Index values over the 1996-1998 survey data period with the Predic ted Trend Value at 2003, would indicate the total potential adjustment, accounting for both the cyclical deviation and trend effects.

Table B3A4.2: Key Results from Analysis of Aft er-Tax Cash Flow Trends by 316(b) Industry for 1992-2003
316(b) Phase III Industry Group Estimated Trend Average of ATCF Index Values at 1996- 1998 Average of Predicted Trend Values at 1996- 1998 Predicted Trend Value at 2003 Aluminum Analysis not undertaken for the Aluminum industry Pulp and Paper Mills -0.00040 0.98953 1.03058 1.02880 Industrial Chemicals 0.00120 1.10059 1.00837 1.01567 Plastics Material and Resins 0.03180 1.03865 0.99332 1.18383 Pharmaceuticals 0.05070 0.98775 0.99703 1.30096 Petroleum Refining 0.02300 0.92346 1.01441 1.15248 Steel -0.00420 1.25879 1.03948 1.01414 Source: U.S. EPA analysis, 2004.
The following page presents charts, by industry group, of the yearly ATCF Index Values and the estimated predicted trend values over the analysis period.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-12 Iron and Steel

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

1 9 9 2
1

9 9 3
1

9 9 4
1

9 9 5
1

9 9 6
1

9 9 7
1

9 9 8
1

9 9 9 2

0 0 0 2

0 0 1 2

0 0 2 2

0 0 3

YearATCF Index Time Trend

Index ATCF Industrial Chemicals

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40

1 9 9 2 1

9 9 3 1

9 9 4 1

9 9 5 1

9 9 6 1

9 9 7 1

9 9 8 1

9 9 9 2

0 0 0 2

0 0 1 2

0 0 2 2

0 0 3

YearATCF Index Time Trend Index ATCF

Petroleum Refining

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3

YearATCF Index Time Trend Index ATCF Pharmaceuticals

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

1 9 9 2
1 9 9 3
1 9 9 4
1 9 9 5
1 9 9 6
1 9 9 7
1 9 9 8
1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3

YearATCF Index Time Trend Index ATCF

Plastic Material and Resins

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40

1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3

YearATCF Index Time Trend Index ATCF Pulp & Paper Mills

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3

YearACTF Index Time Trend Index ATCF Figure B3A4-4: ATCF Index vs Trend§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-13 By industry, these results indicate the following:

< Pulp and Paper Mills . The analysis indicates a very slight annual decline, approximately -0.04 percent, in constant dollar ATCF over the analysis period, m eaning financial performance declined very modestly over this period. The analysis also indicates that the survey data collection years of 1996-1998 showed weaker performance than the predicted trend performan ce in those years. Specifically, the average actual ATCF index value at 1996-98 is 4.0 percent below the predicted ACTF trend value for those years. With negative growth in ATCF over the analysis period, the predicted ATCF index value at the end of the analysis period, 2003, declines slightly from the 1996- 1998 period. As a result, the average actual ATCF index value at 1996-98 is approximately 3.8 per cent below the predicted ACTF trend value in 2003.

< Industrial Chemicals . The analysis for the Industrial Chem icals segment of the Chemicals industry indicates a very slight annual increase, approximately0.12 percent, in constant dollar ATCF over the analysis period, meaning financial performance improved slightly over this period. In contrast to the finding for the Paper and Allied Produc ts industry, the analysis indicates that the Industry Chemicals segment achieved higher financial performance during the survey data collection years of 1996-1998 than the predicted trend performance in those years. Specifically, the average actual ATCF index value at 1996-98 is 9.1 percent above the pred icted ACTF trend value for those years. With slight positive growth in ATCF over the analysis period, the predicted ATCF index value at the end of the analysis period, 2003, increase slightly from the 1996-1998 period. As a result, the average actual ATCF index value at 1996- 98 is approximately 8.4 percent above the predicted ACTF trend value in 2003.

< Plastics Material and Resins . This segment of the Chemicals industry shows a moderate increase, 3.2 percent annually, in constant dollar ATCF over the an alysis period. From this analysis, the Plastic Material and Resins segment, like the Industrial Chemicals segment, also achieved higher financial performance during the survey data collection year s of 1996-1998 than the predicted trend performance in those years. Specifically, the average actual ATCF index value at 1996-98 is 4.6 percent above the predicted ACTF trend value for those years. Howeve r, with relatively strong positive growth in ATCF, the predicted ATCF index value at the end of the analysis period, 2003, increases sufficiently to reverse this relationship. As a result, by 2003, the aver age actual ATCF index value at 1996-98 is 12.3 percent belowthe predicted ACTF trend value in 2003.

< Pharmaceuticals . This third segment of the Chemicals industry also shows a strong increase, 5.1 percent annually, in constant dollar ATCF over the analysis period. Financial performance during the survey data collection years very nearly equaled the predicted ATCF index value: the average ATCF index value at 1996-98 is 0.9 percent below the predicted ACTF trend value for those years. With strong growth in ATCF over the analysis period, by 2003, the predic ted ATCF value is substantially higher than the average ATCF index value at 1996-98: the average ATCF index value at 1996-1998 is 24.1 percent below the predicted trend value at 2003.

< Petroleum Refining . The analysis indicates a moderate increase, 2.3 percent annually, in constant dollar ATCF over the analysis period. The analysis al so indicates that the Petroleum Refining industry achieved, on average, weaker financial performance during the survey data collection years than the predicted trend performance in those years: the av erage ATCF index value for 1996-1998 is 9.0 percent below the predicted trend value during those years. However, the individual yearly values during 1996- 1998 show considerable volatility relativ e to the trend, suggesting weak confidence in this finding. Like Pharmaceuticals, with relatively strong growth in AT CF over the analysis period, by 2003, the predicted ATCF value is substantially higher than the aver age ATCF index value at 1996-98: the average ATCF index value at 1996-1998 is 19.9 percent be low the predicted trend value at 2003.

< Steel . The analysis indicates declining performance, approximately -0.4 per cent annually, in constant dollar ATCF over the analysis period. The analysis also indicates that financial performance during the survey data collection years substantially exceeded, by 21.1 percent, trend-based predicted performance during those years. With declining trend-based pe rformance through the end of the analysis period, by§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-14 2003, the gap between the average ATCF value at 1996-1998 and the predicted trend value at 2003 widens to 24.1 percent. Table B3A4.3, below, summarizes these findings.

Table B3A4.3: Estimated Relationship Between Actual ATCF at Survey Period and Trend Predicted Values at Survey Period and End of Analysis Period
316(b) Phase III Industry Group Percentage Difference in Actual and Predicted ATCF Index Values at 1996- 1998 Percentage Difference in Actual ATCF Index Values at 1996-1998 and Trend Predicted Value at 2003 Aluminum Analysis not undertaken for the Aluminum industry Pulp and Paper Mills -4.0% -3.8%
Industrial Chemicals 9.1% 8.4% Plastics Material and Resins 4.6% -12.3% Pharmaceuticals -0.9% -24.1% Petroleum Refining -9.0% -19.9%
Steel 21.1% 24.1% Source: U.S. EPA analysis, 2004.

From these results, the industries and/or segments where financial performance during the 1996-1998 survey data collection period exceeds trend-predicted performance and thus for which survey data may overstate the industry’s ability to withstand compliance burdens in comparison to the predicted trend values at 1996-1998 are:

< Pulp and Paper Mills industry,

< Industrial Chemicals segment of the Chemicals industry,

< Plastics Material and Resins segment of the Chemicals industry , and

< Steel industry. Looking to the end of the analysis period, 2003, the industries and/or segments where financial performance during the 1996-1998 survey data collection period exceeds trend-predicted performance at 2003, and thus for which survey data may overstate the industry’s ability to withstand compliance burdens at a time closer to the point of regulatory implementation are:

< Industrial Chemicals segment of the Chemicals industry, and

< Steel industry.



### B3A4-4 D EVELOPING AN 

### A DJUSTMENT 

### C ONCEPT On the basis of these findings, EPA considered whether and how to adjust after-tax cash flow, as derived from the facility survey responses for use in the facility impact analysis. Given that several of the industries, or segments within industries, were found to have financial performance during the survey data collection period that exceeded the predicted trend financial performance for that period or that exceeded the predicted trend financial performance at the end of the analysis period, EPA concl uded that development and application of an adjustment to baseline after-tax cash flow was warranted.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-15 In deciding how to adjust cash flow, EPA cons idered three primary adjustment concepts: 1. Adjust baseline cash flow to account for deviation from predicted trend at the time of the survey data collection. 2. Adjust baseline cash flow to account for deviation from predicted trend at the end of the analysis period. 3. Adjust baseline cash flow to a future estimated peri od of compliance based on the estimated trend of change in constant dollar after-tax cash flow over time. EPA decided to apply the ATCF adjustment according to the second of these three adjustment concepts: Adjust baseline cash flow to account for deviation from predicted tre nd at the end of the analysis period. This adjustment concept addresses both concerns that (1) business performan ce during the survey data collection period diverged from the predicted trend performance during the survey data collection period, and (2) business performance from the time of survey data collection period followed a non-ne utral trend to the present. EPA considered extending the trend projection to the estimated time of compliance (concept 3) but rejected this approach since it was deemed speculative in attempting to fo recast business performance into the future. In addition, the greater the number of years over which ATCF results are projected based on historical trend, the less likely that the predicted changes in ATCF reflect the performance of a static set of facilities and instead reflect capital additions, new facilities, facility closures, etc. For these reasons, EPA deci ded to restrict the adjustment to the end of the ATCF analysis period. EPA also considered whether to apply the indicated adjustment factors asymmetrically– i.e., only to reduce the ATCF values as calculated from facility survey responses – or symmetrically– i.e., both to increase or to reduce the ATCF values as calculated from facility survey r esponses. In the case of asymmetric adjustment, the adjustment would correct for business performance during the survey data collection period that exceeded the predicted trend value – whether for the survey period or some future period – and w ould thus attempt to avoid overstating the a facility’s ability to bear the costs of re gulation compliance without material financial impact. In the case of a symmetric adjustment, the adjustment would additionally address the potential that business performance during the survey data collection period fe ll short of the predicted trend value and would thus attempt to avoid understating the ability of an industry or facility to bear the costs of regulatory compliance without material financial impact. On this question, EPA decided to apply the ATCF ad justment on a symmetric basis, potentially reducing or increasing facility cash flow on the basis of the estimated adjustment factor. EPA based its decision on the principle of avoiding both overstatement and understatement of the ability of facilities in an industry to bear the costs of regulatory compliance without material financial impact. Based on these decisions, EPA calculated the adjustment factors by dividing the Predicted Trend Value at 2003 by the Average of ATCF Index Values at 1996-1998 , as reported in Table B3A4-2, above. In the facility closure analysis, as described in Chapter B3: Economic Impact Analysis for Manufacturers , facility after-tax cash flow is simply multiplied by the appropriate adjustment factor based on the industry or industry segment to which a facility is assigned. The resulting adjusted ATCF is carried forward in the baseline and post-compliance closure analyses. Where the Predicted Trend Value at 2003 is less than Average of ATCF Index Values at 1996-1998 , the resulting adjustment factor value is less than one and the e ffect of the ATCF adjustment is to reduce the calculated value of cash flow used in the f acility impact analysis. Where the Predicted Trend Value at 2003 is greater than Average of ATCF Index Values at 1996-1998 , the resulting adjustment factor value is greater than one and the effect of the ATCF adjustment is to increase the calculated value of cash flow used in the facility impact analysis. EPA also used the adjustment factors to adjust the numerator values of the measures used in the facility moderate impact analysis: pre-tax return on assets and interest cove rage ratio. In both cases, the numerators of the measures are closely related to cash flow, but are calculated on a pre-tax basis. As a result, in this case, the ATCF-based adjustment factor does not match as closely in concept th e financial measures to which it is applied as is the case for the ATCF measure used in the f acility closure analysis. Nevertheless, use of the adjustment for these§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-16 measures is appropriate because the pre-tax measures used in the facility moderate impact analysis will still move closely with the after-tax cash flow measure on which the ad justment factor is based. In addition, it is important that EPA recognize the potential effect of change in fina ncial condition since the survey data collection period in the facility moderate impact analysis as well as in the facility closure analysis. Table B3A4.4, below, summarizes the adjustment factors im plemented for each of the industries, and within the chemical industry, for the industry segments. The tabl e also reports the number of baseline and post-compliance closures (under the proposed regulatory option) estimated with and without application of the ATCF adjustment factor.

Table B3A4.4: Using After-Tax Cash Flow Adjust ment Factors in the Facility Closure Analysis

Adjustment Factor Number of Facilities Analyzed Summary Results from Closure Analysis Using Adjustment Factors Not Using Factors Baseline Closures Regulatory Closures Baseline Closures Regulatory Closures Aluminum 1.0000 21 7 0 7 0 Pulp and Paper Mills 1.0397 230 32 0 32 0 Chemicals Industry Industrial Chemicals 0.9228 138 4040 Plastics Material and Resins 1.1398 34 0000
Pharmaceuticals 1.3171 6 0000 Petroleum Refining 1.2480 36 5051 Steel 0.8056 68 29 0 25 0 Total 532 76 0 73 1 Adjustment factor not developed for Aluminum industry, so the results with use of adjustment factors are necessarily the same as those without use of the factors. All results are sample weighted. The reported totals may di ffer from the apparent sums of individual data items due to rounding. Source: U.S. EPA analysis, 2004.
As reported in Table B3A4.4, the adjustment factors fo r the Steel industry and Industrial Chemicals segment of the Chemicals industry are less than one, at 0.8056 and 0.9228, respectively. For these industries, the effect of the adjustment factor is to reduce the cash flow values calcu lated from facility survey responses. As described above, EPA did not calculate an adjustment factor for the Aluminum industry due to data limitations; accordingly, this industry’s “adjustment factor” is simply 1.0000. The adjustment factors for the remaining industries are greater than one, ranging from a value of 1.0397 for the Pulp and Paper Mills industry to 1.3198 for the Pharmaceuticals segment of the Chemicals industry. For these industries, th e effect of the adjustment factor is to increase the cash flow values calculated from facility survey responses. In terms of effect on analytic results, the use of th e ATCF adjustment factors caused the number of baseline closures to change in only one 316(b) industry group, the Steel industry. For this industry, the reduction in cash flow resulting from the adjustment causes an additional 4 facilities, on a sample weighted basis, to fail the baseline closure analysis.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-17 Under the 50 MGD All Option for existing manufacturing facilities, the use of the ATCF adjustment factors eliminated one regulatory closure, in the Petroleum Re fining industry. Because the calculated adjustment factor for this industry is quite large, 1.248, EPA reviewed cl osely the effect of the adjustment factor on the facility closure calculation. In particular, EPA was concerne d that the regulatory closure was being eliminated by application of a large ATCF adjustment. From this re view, EPA determined that the single Petroleum Refining industry closure, without the ATCF adjustment , is a very marginal closure. Specifically, an ATCF adjustment factor of 1.0195 (compared to the calculated 1.248) provides a sufficient increase in baseline cash flow to eliminate the closure under the compliance requirements of the 50 MGD All Option. Another way of understanding the 1.0195 adjustment factor is to calculate the annual trend factor (year-to-year change in predicted trend ATCF Index) that would yield the 1.0195 va lue. In this case, the year-to-year change required to generate the 1.0195 value is 0.0038, or an annual change factor that is very close to zero (approximately 0.38 percent annual average change). Given these findings, EPAconcludes that the extent of improvement in cash flow needed to eliminate the regulatory closure in the Petroleum Refining industry is extremely small and is thus quite plausible within the overall improving business pe rformance trend exhibited by the Petroleum Refining industry. Under the other two co-proposed options, the 200 MGD All Option and the 100 MGD SWB Option, the use of the ATCF adjustment factors would have no closure effect.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 4 to Chapter B3

B3A4-18 

### REFERENCES Board of Governors of the Federal Reserve System. Release G.17, Industrial Production and Capacity Utilization: Capacity Utiliza tion: Manufacturing (NAICS) and Industrial Production Indexquarterly data series. 2004. U.S. Department of Commerce. Bureau of Economic Analysis. Table 1.1.6. Real Gross Domestic Product, Chained Dollars . 2004. U.S. Environmental Protection Agency (U.S. EPA). 2004. Information Collection Request for Cooling Water Intake Structures, Phase II Existing Facility Final Rule . ICR Number 2060.02. February 2004. Value Line Investment Survey. Value Line Publishing, Inc. 220 East 42nd Street, New York, N.Y. 10017-589. 2003 and 2004.